Dissertations / Theses on the topic 'Hidden state Markov model'
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Beattie, Valerie L. "Hidden Markov Model state-based noise compensation." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259519.
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Baribault, Carl. "Meta State Generalized Hidden Markov Model for Eukaryotic Gene Structure Identification." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/1098.
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Using a generalized-clique hidden Markov model (HMM) as the starting point for a eukaryotic gene finder, the objective here is to strengthen the signal information at the transitions between coding and non-coding (c/nc) regions. This is done by enlarging the primitive hidden states associated with individual base labeling (as exon, intron, or junk) to substrings of primitive hidden states or footprint states. Moreover, the allowed footprint transitions are restricted to those that include either one c/nc transition or none at all. (This effectively imposes a minimum length on exons and the other regions.) These footprint states allow the c/nc transitions to be seen sooner and have their contributions to the gene-structure identification weighted more heavily – yet contributing as such with a natural weighting determined by the HMM model itself according to the training data – rather than via introducing an artificial gain-parameter tuning on major transitions. The selection of the generalized HMM model is interpolated to highest Markov order on emission probabilities, and to highest Markov order (subsequence length) on the footprint states. The former is accomplished via simple count cutoff rules, the latter via an identification of anomalous base statistics near the major transitions using Shannon entropy. Preliminary indications, from applications to the C. elegans genome, are that the sensitivity/specificity (SN/SP) result for both the individual state and full exon predictions are greatly enhanced using the generalized-clique HMM when compared to the standard HMM. Here the standard HMM is represented by the choice of the smallest size of footprint state in the generalized-clique HMM. Even with these improvements, we observe that both extremely long and short exon and intron segments would go undetected without an explicit model of the duration of state. The key contributions of this effort are the full derivation and experimental confirmation of a rudimentary, yet powerful and competitive gene finding method based on a higher order hidden Markov model. With suitable extensions, this method is expected to provide superior gene finding capability – not only in the context of pre-conditioned data sets as in the evaluations cited but also in the wider context of less preconditioned and/or raw genomic data.
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Florez-Larrahondo, German. "Incremental learning of discrete hidden Markov models." Diss., Mississippi State : Mississippi State University, 2005. http://library.msstate.edu/etd/show.asp?etd=etd-05312005-141645.
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Pepper, David J. "Large hidden Markov model state interpretation as applied to automatic phonetic segmentation and labeling." Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/13537.
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Santos, Leonor Marques Pompeu dos. "Hidden Markov models for credit risk." Master's thesis, Instituto Superior de Economia e Gestão, 2015. http://hdl.handle.net/10400.5/11061.
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Mestrado em Matemática FinanceiraA análise do Risco de Crédito, a avaliação do risco de defafult ou de redução do valor de mercado causado por alterações na qualidade de crédito, tem sido um tema vastamente estudado ao longo dos últimos trinta anos e é hoje mais relevante que nunca, com o mundo ainda a recuperar das consequências de uma crise financeira, na sua génese induzida por uma observação imperfeita deste tipo de risco. Tal como alguns dos modelos apresentados anteriormente, o modelo apresentado nesta dissertação assume que os eventos de default estão directamente ligados a uma variável associada ao risco, partindo de um modelo simples que assume que o default segue um Modelo Oculto de Markov Binomial de dois estados, ou seja, um modelo que considera apenas dois "estados de risco" possíveis para explicar na totalidade a ocorrência de default, e aproximando-o a um Modelo Oculto de Markov Poisson, com todas as simplificações computacionais associadas a esta aproximação, tentando, ao mesmo tempo, traduzir o modelo para um cenário menos extremo, com a inclusão de um nível de risco intermédio.
Credit Risk measurement, the evaluation of the risk of default or reduction in market value caused by changes in credit quality, has been a broadly studied subject over the last thirty years and is now more relevant than ever, when the world is still suffering the consequences of the break of a financial crisis in its genesis induced by a false observation of this kind of risk. Just like some of the previous studies, the model presented in this dissertation assumes that default events are directly connected to risk state variables, starting from a very simple model that assumes defaults to follow a two-state Binomial Hidden Markov Model, considering only two different risk categories to fully explain default occurrence, and approximating it to a Poisson Hidden Markov Model, with all the computational simplifications brought by this approximation, trying, at the same time, to translate the model into a less extreme framework, with the addition of an intermediate risk level, a "normal" risk state.
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Xie, Li Information Technology & Electrical Engineering Australian Defence Force Academy UNSW. "Finite horizon robust state estimation for uncertain finite-alphabet hidden Markov models." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Information Technology and Electrical Engineering, 2004. http://handle.unsw.edu.au/1959.4/38664.
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In this thesis, we consider a robust state estimation problem for discrete-time, homogeneous, first-order, finite-state finite-alphabet hidden Markov models (HMMs). Based on Kolmogorov's Theorem on the existence of a process, we first present the Kolmogorov model for the HMMs under consideration. A new change of measure is introduced. The statistical properties of the Kolmogorov representation of an HMM are discussed on the canonical probability space. A special Kolmogorov measure is constructed. Meanwhile, the ergodicity of two expanded Markov chains is investigated. In order to describe the uncertainty of HMMs, we study probability distance problems based on the Kolmogorov model of HMMs. Using a change of measure technique, the relative entropy and the relative entropy rate as probability distances between HMMs, are given in terms of the HMM parameters. Also, we obtain a new expression for a probability distance considered in the existing literature such that we can use an information state method to calculate it. Furthermore, we introduce regular conditional relative entropy as an a posteriori probability distance to measure the discrepancy between HMMs when a realized observation sequence is given. A representation of the regular conditional relative entropy is derived based on the Radon-Nikodym derivative. Then a recursion for the regular conditional relative entropy is obtained using an information state method. Meanwhile, the well-known duality relationship between free energy and relative entropy is extended to the case of regular conditional relative entropy given a sub-[special character]-algebra. Finally, regular conditional relative entropy constraints are defined based on the study of the probability distance problem. Using a Lagrange multiplier technique and the duality relationship for regular conditional relative entropy, a finite horizon robust state estimator for HMMs with regular conditional relative entropy constraints is derived. A complete characterization of the solution to the robust state estimation problem is also presented.
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Wieworka, Adam. "Speech recognition using Hidden Markov Models with exponential interpolation of state parameters." Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286612.
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Blix, Magnus. "Essays in mathematical finance : modeling the futures price." Doctoral thesis, Handelshögskolan i Stockholm, Finansiell Ekonomi (FI), 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-534.
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This thesis consists of four papers dealing with the futures price process. In the first paper, we propose a two-factor futures volatility model designed for the US natural gas market, but applicable to any futures market where volatility decreases with maturity and varies with the seasons. A closed form analytical expression for European call options is derived within the model and used to calibrate the model to implied market volatilities. The result is used to price swaptions and calendar spread options on the futures curve. In the second paper, a financial market is specified where the underlying asset is driven by a d-dimensional Wiener process and an M dimensional Markov process. On this market, we provide necessary and, in the time homogenous case, sufficient conditions for the futures price to possess a semi-affine term structure. Next, the case when the Markov process is unobservable is considered. We show that the pricing problem in this setting can be viewed as a filtering problem, and we present explicit solutions for futures. Finally, we present explicit solutions for options on futures both in the observable and unobservable case. The third paper is an empirical study of the SABR model, one of the latest contributions to the field of stochastic volatility models. By Monte Carlo simulation we test the accuracy of the approximation the model relies on, and we investigate the stability of the parameters involved. Further, the model is calibrated to market implied volatility, and its dynamic performance is tested. In the fourth paper, co-authored with Tomas Björk and Camilla Landén, we consider HJM type models for the term structure of futures prices, where the volatility is allowed to be an arbitrary smooth functional of the present futures price curve. Using a Lie algebraic approach we investigate when the infinite dimensional futures price process can be realized by a finite dimensional Markovian state space model, and we give general necessary and sufficient conditions, in terms of the volatility structure, for the existence of a finite dimensional realization. We study a number of concrete applications including the model developed in the first paper of this thesis. In particular, we provide necessary and sufficient conditions for when the induced spot price is a Markov process. We prove that the only HJM type futures price models with spot price dependent volatility structures, generically possessing a spot price realization, are the affine ones. These models are thus the only generic spot price models from a futures price term structure point of view.Diss. Stockholm : Handelshögskolan, 2004
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Baker, Adam. "Temporal dynamics of resting state brain connectivity as revealed by magnetoencephalography." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:ad9a825f-7036-4597-89d3-a7dfc8bb0641.
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Explorations into the organisation of spontaneous activity within the brain have demonstrated the existence of networks of temporally correlated activity, consisting of brain areas that share similar cognitive or sensory functions. These so-called resting state networks (RSNs) emerge spontaneously during rest and disappear in response to overt stimuli or cognitive demands. In recent years, the study of RSNs has emerged as a valuable tool for probing brain function, both in the healthy brain and in disorders such as schizophrenia, Alzheimer’s disease and Parkinson’s disease. However, analyses of these networks have so far been limited, in part due to assumptions that the patterns of neuronal activity that underlie these networks remain constant over time. Moreover, the majority of RSN studies have used functional magnetic resonance imaging (fMRI), in which slow fluctuations in the level of oxygen in the blood are used as a proxy for the activity within a given brain region. In this thesis we develop the use of magnetoencephalography (MEG) to study resting state functional connectivity. Unlike fMRI, MEG provides a direct measure of neuronal activity and can provide novel insights into the temporal dynamics that underlie resting state activity. In particular, we focus on the application of non- stationary analysis methods, which are able to capture fast temporal changes in activity. We first develop a framework for preprocessing MEG data and measuring interactions within different RSNs (Chapter 3). We then extend this framework to assess temporal variability in resting state functional connectivity by applying time- varying measures of interactions and show that within-network functional connectivity is underpinned by non-stationary temporal dynamics (Chapter 4). Finally we develop a data driven approach based on a hidden Markov model for inferring short lived connectivity states from resting state and task data (Chapter 5). By applying this approach to data from multiple subjects we reveal transient states that capture short lived patterns of neuronal activity (Chapter 6).
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Ejnestrand, Ida, and Linnéa Jakobsson. "Object Tracking based on Eye Tracking Data : A comparison with a state-of-the-art video tracker." Thesis, Linköpings universitet, Datorseende, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166007.
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The process of locating moving objects through video sequences is a fundamental computer vision problem. This process is referred to as video tracking and has a broad range of applications. Even though video tracking is an open research topic that have received much attention during recent years, developing accurate and robust algorithms that can handle complicated tracking tasks and scenes is still challenging. One challenge in computer vision is to develop systems that like humans can understand, interpret and recognize visual information in different situations. In this master thesis work, a tracking algorithm based on eye tracking data is proposed. The aim was to compare the tracking performance of the proposed algorithm with a state-of-the-art video tracker. The algorithm was tested on gaze signals from five participants recorded with an eye tracker while the participants were exposed to dynamic stimuli. The stimuli were moving objects displayed on a stationary computer screen. The proposed algorithm is working offline meaning that all data is collected before analysis. The results show that the overall performance of the proposed eye tracking algorithm is comparable to the performance of a state-of-the-art video tracker. The main weaknesses are low accuracy for the proposed eye tracking algorithm and handling of occlusion for the video tracker. We also suggest a method for using eye tracking as a complement to object tracking methods. The results show that the eye tracker can be used in some situations to improve the tracking result of the video tracker. The proposed algorithm can be used to help the video tracker to redetect objects that have been occluded or for some other reason are not detected correctly. However, ATOM brings higher accuracy.
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McGillivray, Annaliza. "A penalized quasi-likelihood approach for estimating the number of states in a hidden markov model." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=110634.
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In statistical applications of hidden Markov models (HMMs), one may have no knowledge of the number of hidden states (or order) of the model needed to be able to accurately represent the underlying process of the data. The problem of estimating the number of states of the HMM is thus a task of major importance. We begin with a literature review of the major developments in the problem of order estimation for HMMs. We then propose a new penalized quasi-likelihood method for estimating the number of hidden states, which makes use of the fact that the marginal distribution of the HMM observations is a finite mixture model. Starting with a HMM with a large number of states, the method obtains a model of lower order by clustering and merging similar states of the model through two penalty functions. We study some of the asymptotic properties of the proposed method and present a numerical procedure for its implementation. The performance of the new method is assessed via extensive simulation studies for normal and Poisson HMMs. The new method is more computationally efficient than existing methods, such as AIC and BIC, as the order of the model is determined in a single optimization. We conclude with applications of the method to two real data sets.Dans les applications des chaînes de Markov cachées (CMC), il se peut que les statisticiens n'aient pas l'information sur le nombre d'états (ou ordre) nécessaires pour représenter le processus. Le problème d'estimer le nombre d'états du CMC est ainsi une tâche d'importance majeure. Nous commençons avec une revue de littérature des développements majeurs dans le problème d'estimation de l'ordre d'un CMC. Nous proposons alors une nouvelle méthode de la quasi-vraisemblance pénalisée pour estimer l'ordre dans des CMC. Cette méthode utilise le fait que la distribution marginale des observations CMC est un mélange fini. La méthode débute avec un CMC avec un grand nombre d'états et obtient un modèle d'ordre inférieur en regroupant et fusionnant les états à l'aide de deux fonctions de pénalité. Nous étudions certaines propriétés asymptotiques de la méthode proposée et présentons une procédure numérique pour sa mise en œuvre. La performance est évaluée via des simulations extensives. La nouvelle méthode est plus efficace qu'autres méthodes, comme CIA et CIB, comme l'ordre du modèle est déterminé dans une seule optimisation. Nous concluons avec l'application de la méthode à deux vrais jeux de données.
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Nearing, Grey Stephen. "Diagnostics and Generalizations for Parametric State Estimation." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/293533.
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This dissertation is comprised of a collection of five distinct research projects which apply, evaluate and extend common methods for land surface data assimilation. The introduction of novel diagnostics and extensions of existing algorithms is motivated by an example, related to estimating agricultural productivity, of failed application of current methods. We subsequently develop methods, based on Shannon's theory of communication, to quantify the contributions from all possible factors to the residual uncertainty in state estimates after data assimilation, and to measure the amount of information contained in observations which is lost due to erroneous assumptions in the assimilation algorithm. Additionally, we discuss an appropriate interpretation of Shannon information which allows us to measure the amount of information contained in a model, and use this interpretation to measure the amount of information introduced during data assimilation-based system identification. Finally, we propose a generalization of the ensemble Kalman filter designed to alleviate one of the primary assumptions - that the observation function is linear.
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Andersson, Lovisa. "An application of Bayesian Hidden Markov Models to explore traffic flow conditions in an urban area." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385187.
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This study employs Bayesian Hidden Markov Models as method to explore vehicle traffic flow conditions in an urban area in Stockholm, based on sensor data from separate road positions. Inter-arrival times are used as the observed sequences. These sequences of inter-arrival times are assumed to be generated from the distributions of four different (and hidden) traffic flow states; nightly free flow, free flow, mixture and congestion. The filtered and smoothed probability distributions of the hidden states and the most probable state sequences are obtained by using the forward, forward-backward and Viterbi algorithms. The No-U-Turn sampler is used to sample from the posterior distributions of all unknown parameters. The obtained results show in a satisfactory way that the Hidden Markov Models can detect different traffic flow conditions. Some of the models have problems with divergence, but the obtained results from those models still show satisfactory results. In fact, two of the models that converged seemed to overestimate the presence of congested traffic and all the models that not converged seem to do adequate estimations of the probability of being in a congested state. Since the interest of this study lies in estimating the current traffic flow condition, and not in doing parameter inference, the model choice of Bayesian Hidden Markov Models is satisfactory. Due to the unsupervised nature of the problematization of this study, it is difficult to evaluate the accuracy of the results. However, a model with simulated data and known states was also implemented, which resulted in a high classification accuracy. This indicates that the choice of Hidden Markov Models is a good model choice for estimating traffic flow conditions.
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Seward, Alexander. "Efficient Methods for Automatic Speech Recognition." Doctoral thesis, KTH, Tal, musik och hörsel, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3675.
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This thesis presents work in the area of automatic speech recognition (ASR). The thesis focuses on methods for increasing the efficiency of speech recognition systems and on techniques for efficient representation of different types of knowledge in the decoding process. In this work, several decoding algorithms and recognition systems have been developed, aimed at various recognition tasks. The thesis presents the KTH large vocabulary speech recognition system. The system was developed for online (live) recognition with large vocabularies and complex language models. The system utilizes weighted transducer theory for efficient representation of different knowledge sources, with the purpose of optimizing the recognition process. A search algorithm for efficient processing of hidden Markov models (HMMs) is presented. The algorithm is an alternative to the classical Viterbi algorithm for fast computation of shortest paths in HMMs. It is part of a larger decoding strategy aimed at reducing the overall computational complexity in ASR. In this approach, all HMM computations are completely decoupled from the rest of the decoding process. This enables the use of larger vocabularies and more complex language models without an increase of HMM-related computations. Ace is another speech recognition system developed within this work. It is a platform aimed at facilitating the development of speech recognizers and new decoding methods. A real-time system for low-latency online speech transcription is also presented. The system was developed within a project with the goal of improving the possibilities for hard-of-hearing people to use conventional telephony by providing speech-synchronized multimodal feedback. This work addresses several additional requirements implied by this special recognition task.QC 20100811
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Vural, Gurkan. "Anomaly Detection From Personal Usage Patterns In Web Applications." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607973/index.pdf.
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The anomaly detection task is to recognize the presence of an unusual (and potentially hazardous) state within the behaviors or activities of a computer user, system, or network with respect to some model of normal behavior which may be either hard-coded or learned from observation. An anomaly detection agent faces many learning problems including learning from streams of temporal data, learning from instances of a single class, and adaptation to a dynamically changing concept. The domain is complicated by considerations of the trusted insider problem (recognizing the difference between innocuous and malicious behavior changes on the part of a trusted user).
This study introduces the anomaly detection in web applications and formulates it as a machine learning task on temporal sequence data. In this study the goal is to develop a model or profile of normal working state of web application user and to detect anomalous conditions as deviations from the expected behavior patterns. We focus, here, on learning models of normality at the user behavioral level, as observed through a web application. In this study we introduce some sensors intended to function as a focus of attention unit at the lowest level of a classification hierarchy using Finite State Markov Chains and Hidden Markov Models and discuss the success of these sensors.
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Medeiros, Francisco Mois?s C?ndido de. "Estima??o param?trica e n?o-param?trica em modelos de markov ocultos." Universidade Federal do Rio Grande do Norte, 2010. http://repositorio.ufrn.br:8080/jspui/handle/123456789/18630.
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In this work we study the Hidden Markov Models with finite as well as general state space. In the finite case, the forward and backward algorithms are considered and the probability of a given observed sequence is computed. Next, we use the EM algorithm to estimate the model parameters. In the general case, the kernel estimators are used and to built a sequence of estimators that converge in L1-norm to the density function of the observable process
Neste trabalho estudamos os modelos de Markov ocultos tanto em espa?o de estados finito quanto em espa?o de estados geral. No caso discreto, estudamos os algoritmos para frente e para tr?s para determinar a probabilidade da sequ?ncia observada e, em seguida, estimamos os par?metros do modelo via algoritmo EM. No caso geral, estudamos os estimadores do tipo n?cleo e os utilizamos para conseguir uma sequ?ncia de estimadores que converge na norma L1 para a fun??o densidade do processo observado
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Andrés, Ferrer Jesús. "Statistical approaches for natural language modelling and monotone statistical machine translation." Doctoral thesis, Universitat Politècnica de València, 2010. http://hdl.handle.net/10251/7109.
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Esta tesis reune algunas contribuciones al reconocimiento de formas estadístico y, más especícamente, a varias tareas del procesamiento del lenguaje natural. Varias técnicas estadísticas bien conocidas se revisan en esta tesis, a saber: estimación paramétrica, diseño de la función de pérdida y modelado estadístico. Estas técnicas se aplican a varias tareas del procesamiento del lenguajes natural tales como clasicación de documentos, modelado del lenguaje natural
y traducción automática estadística.
En relación con la estimación paramétrica, abordamos el problema del suavizado proponiendo una nueva técnica de estimación por máxima verosimilitud con dominio restringido (CDMLEa ). La técnica CDMLE evita la necesidad de la etapa de suavizado que propicia la pérdida de las propiedades del estimador máximo verosímil. Esta técnica se aplica a clasicación de documentos mediante el clasificador Naive Bayes. Más tarde, la técnica CDMLE se extiende a la estimación por máxima verosimilitud por leaving-one-out aplicandola al suavizado de modelos de lenguaje. Los resultados obtenidos en varias tareas de modelado del lenguaje natural, muestran una mejora en términos de perplejidad.
En a la función de pérdida, se estudia cuidadosamente el diseño de funciones de pérdida diferentes a la 0-1. El estudio se centra en aquellas funciones de pérdida que reteniendo una complejidad de decodificación similar a la función 0-1, proporcionan una mayor flexibilidad. Analizamos y presentamos varias funciones de pérdida en varias tareas de traducción automática y con varios modelos de traducción. También, analizamos algunas reglas de traducción que destacan por causas prácticas tales como la regla de traducción directa; y, así mismo, profundizamos en la comprensión de los modelos log-lineares, que son de hecho, casos particulares de funciones de pérdida.
Finalmente, se proponen varios modelos de traducción monótonos basados en técnicas de modelado estadístico .Andrés Ferrer, J. (2010). Statistical approaches for natural language modelling and monotone statistical machine translation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7109
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Kotsalis, Georgios. "Model reduction for Hidden Markov models." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38255.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.Includes bibliographical references (leaves 57-60).
The contribution of this thesis is the development of tractable computational methods for reducing the complexity of two classes of dynamical systems, finite alphabet Hidden Markov Models and Jump Linear Systems with finite parameter space. The reduction algorithms employ convex optimization and numerical linear algebra tools and do not pose any structural requirements on the systems at hand. In the Jump Linear Systems case, a distance metric based on randomization of the parametric input is introduced. The main point of the reduction algorithm lies in the formulation of two dissipation inequalities, which in conjunction with a suitably defined storage function enable the derivation of low complexity models, whose fidelity is controlled by a guaranteed upper bound on the stochastic L2 gain of the approximation error. The developed reduction procedure can be interpreted as an extension of the balanced truncation method to the broader class of Jump Linear Systems. In the Hidden Markov Model case, Hidden Markov Models are identified with appropriate Jump Linear Systems that satisfy certain constraints on the coefficients of the linear transformation. This correspondence enables the development of a two step reduction procedure.
(cont.) In the first step, the image of the high dimensional Hidden Markov Model in the space of Jump Linear Systems is simplified by means of the aforementioned balanced truncation method. Subsequently, in the second step, the constraints that reflect the Hidden Markov Model structure are imposed by solving a low dimensional non convex optimization problem. Numerical simulation results provide evidence that the proposed algorithm computes accurate reduced order Hidden Markov Models, while achieving a compression of the state space by orders of magnitude.
by Georgios Kotsalis.
Ph.D.
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Schimert, James. "A high order hidden Markov model /." Thesis, Connect to this title online; UW restricted, 1992. http://hdl.handle.net/1773/8939.
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Chong, Fong Ho. "Frequency-stream-tying hidden Markov model /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202003%20CHONG.
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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.Includes bibliographical references (leaves 119-123). Also available in electronic version. Access restricted to campus users.
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Kato, Akihiro. "Hidden Markov model-based speech enhancement." Thesis, University of East Anglia, 2017. https://ueaeprints.uea.ac.uk/63950/.
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This work proposes a method of model-based speech enhancement that uses a network of HMMs to first decode noisy speech and to then synthesise a set of features that enables a speech production model to reconstruct clean speech. The motivation is to remove the distortion and residual and musical noises that are associated with conventional filteringbased methods of speech enhancement. STRAIGHT forms the speech production model for speech reconstruction and requires a time-frequency spectral surface, aperiodicity and a fundamental frequency contour. The technique of HMM-based synthesis is used to create the estimate of the timefrequency surface, and aperiodicity after the model and state sequence is obtained from HMM decoding of the input noisy speech. Fundamental frequency were found to be best estimated using the PEFAC method rather than synthesis from the HMMs. For the robust HMM decoding in noisy conditions it is necessary for the HMMs to model noisy speech and consequently noise adaptation is investigated to achieve this and its resulting effect on the reconstructed speech measured. Even with such noise adaptation to match the HMMs to the noisy conditions, decoding errors arise, both in terms of incorrect decoding and time alignment errors. Confidence measures are developed to identify such errors and then compensation methods developed to conceal these errors in the enhanced speech signal. Speech quality and intelligibility analysis is first applied in terms of PESQ and NCM showing the superiority of the proposed method against conventional methods at low SNRs. Three way subjective MOS listening test then discovers the performance of the proposed method overwhelmingly surpass the conventional methods over all noise conditions and then a subjective word recognition test shows an advantage of the proposed method over speech intelligibility to the conventional methods at low SNRs.
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Stanke, Mario. "Gene prediction with a Hidden Markov model." Doctoral thesis, [S.l.] : [s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=970841310.
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Lott, Paul Christian. "StochHMM| A Flexible Hidden Markov Model Framework." Thesis, University of California, Davis, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3602142.
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In the era of genomics, data analysis models and algorithms that provide the means to reduce large complex sets into meaningful information are integral to further our understanding of complex biological systems. Hidden Markov models comprise one such data analysis technique that has become the basis of many bioinformatics tools. Its relative success is primarily due to its conceptually simplicity and robust statistical foundation. Despite being one of the most popular data analysis modeling techniques for classification of linear sequences of data, researchers have few available software options to rapidly implement the necessary modeling framework and algorithms. Most tools are still hand-coded because current implementation solutions do not provide the required ease or flexibility that allows researchers to implement models in non-traditional ways. I have developed a free hidden Markov model C++ library and application, called StochHMM, that provides researchers with the flexibility to apply hidden Markov models to unique sequence analysis problems. It provides researchers the ability to rapidly implement a model using a simple text file and at the same time provide the flexibility to adapt the model in non-traditional ways. In addition, it provides many features that are not available in any current HMM implementation tools, such as stochastic sampling algorithms, ability to link user-defined functions into the HMM framework, and multiple ways to integrate additional data sources together to make better predictions. Using StochHMM, we have been able to rapidly implement models for R-loop prediction and classification of methylation domains. The R-loop predictions uncovered the epigenetic regulatory role of R-loops at CpG promoters and protein coding genes 3' transcription termination. Classification of methylation domains in multiple pluripotent tissues identified epigenetics gene tracks that will help inform our understanding of epigenetic diseases.
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Yi, Kwan 1963. "Text classification using a hidden Markov model." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85214.
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Text categorization (TC) is the task of automatically categorizing textual digital documents into pre-set categories by analyzing their contents. The purpose of this study is to develop an effective TC model to resolve the difficulty of automatic classification. In this study, two primary goals are intended. First, a Hidden Markov Model (HAM is proposed as a relatively new method for text categorization. HMM has been applied to a wide range of applications in text processing such as text segmentation and event tracking, information retrieval, and information extraction. Few, however, have applied HMM to TC. Second, the Library of Congress Classification (LCC) is adopted as a classification scheme for the HMM-based TC model for categorizing digital documents. LCC has been used only in a handful of experiments for the purpose of automatic classification. In the proposed framework, a general prototype for an HMM-based TC model is designed, and an experimental model based on the prototype is implemented so as to categorize digitalized documents into LCC. A sample of abstracts from the ProQuest Digital Dissertations database is used for the test-base. Dissertation abstracts, which are pre-classified by professional librarians, form an ideal test-base for evaluating the proposed model of automatic TC. For comparative purposes, a Naive Bayesian model, which has been extensively used in TC applications, is also implemented. Our experimental results show that the performance of our model surpasses that of the Naive Bayesian model as measured by comparing the automatic classification of abstracts to the manual classification performed by professionals.
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Seward, D. C. (DeWitt Clinton). "Graphical analysis of hidden Markov model experiments." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/36469.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaves 60-61).
by DeWitt C. Seward IV.
Ph.D.
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Kadhem, Safaa K. "Model fit diagnostics for hidden Markov models." Thesis, University of Plymouth, 2017. http://hdl.handle.net/10026.1/9966.
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Hidden Markov models (HMMs) are an efficient tool to describe and model the underlying behaviour of many phenomena. HMMs assume that the observed data are generated independently from a parametric distribution, conditional on an unobserved process that satisfies the Markov property. The model selection or determining the number of hidden states for these models is an important issue which represents the main interest of this thesis. Applying likelihood-based criteria for HMMs is a challenging task as the likelihood function of these models is not available in a closed form. Using the data augmentation approach, we derive two forms of the likelihood function of a HMM in closed form, namely the observed and the conditional likelihoods. Subsequently, we develop several modified versions of the Akaike information criterion (AIC) and Bayesian information criterion (BIC) approximated under the Bayesian principle. We also develop several versions for the deviance information criterion (DIC). These proposed versions are based on the type of likelihood, i.e. conditional or observed likelihood, and also on whether the hidden states are dealt with as missing data or additional parameters in the model. This latter point is referred to as the concept of focus. Finally, we consider model selection from a predictive viewpoint. To this end, we develop the so-called widely applicable information criterion (WAIC). We assess the performance of these various proposed criteria via simulation studies and real-data applications. In this thesis, we apply Poisson HMMs to model the spatial dependence analysis in count data via an application to traffic safety crashes for three highways in the UK. The ultimate interest is in identifying highway segments which have distinctly higher crash rates. Selecting an optimal number of states is an important part of the interpretation. For this purpose, we employ model selection criteria to determine the optimal number of states. We also use several goodness-of-fit checks to assess the model fitted to the data. We implement an MCMC algorithm and check its convergence. We examine the sensitivity of the results to the prior specification, a potential problem given small sample sizes. The Poisson HMMs adopted can provide a different model for analysing spatial dependence on networks. It is possible to identify segments with a higher posterior probability of classification in a high risk state, a task that could prioritise management action.
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Schwardt, Ludwig. "Efficient Mixed-Order Hidden Markov Model Inference." Thesis, Link to the online version, 2007. http://hdl.handle.net/10019/709.
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Ford, Jason. "Adaptive hidden Markov model estimation and applications." Phd thesis, Australian National University, 1998. http://hdl.handle.net/1885/145631.
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Initially introduced in the late 1960's and early 1970's, hidden Markov models (HMMs) have become increasingly popular in the last decade. The major reason for the increasing popularity of HMMs has been the richness of the model class and the power of the signal processing tools.
In this thesis we propose several algorithms for estimation of HMM parameters. Initially, we propose recursive prediction error algorithms for separately estimating the state values and the state transition probability matrix. Local convergence results and corresponding convergence rates are obtained via an ordinary differential equation (ODE) approach. Suboptimal extended least squares algorithms are also presented and convergence results are established in idealized situations. These algorithms exploit the discrete-valued nature of HMMs.
Following this, globally convergent parameter estimators for HMMs are presented. These estimators have parallels to the well known Baum-Welch EM algorithm for off-line estimation of HMM parameters. Almost sure convergence results and convergence rates results are established using martingale convergence results, the Kronecker lemma and an ODE approach. This inspires the proposal of globally convergent parameter estimators for partially observed linear systems and hybrid linear systems. Almost sure convergence results are established using martingale convergence results, the Kronecker lemma and an ODE approach. Finally, as a contribution towards applications, optimal HMM filters are developed for demodulation of differentially encoded transmission systems and a decision feedback equalizer is proposed.
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Ford, Jason. "Adaptive hidden Markov model estimation and applications." Thesis, Australian National University, 1998. https://eprints.qut.edu.au/108491/1/jasonford.pdf.
Full textAbstract:
Initially introduced in the late 1960's and early 1970's, hidden Markov models (HMMs) have become increasingly popular in the last decade. The major reason for the increasing popularity of HMMs has been the richness of the model class and the power of the signal processing tools.
In this thesis we propose several algorithms for estimation of HMM parameters. Initially, we propose recursive prediction error algorithms for separately estimating the state values and the state transition probability matrix. Local convergence results and corresponding convergence rates are obtained via an ordinary differential equation (ODE) approach. Suboptimal extended least squares algorithms are also presented and convergence results are established in idealized situations. These algorithms exploit the discrete-valued nature of HMMs.
Following this, globally convergent parameter estimators for HMMs are presented. These estimators have parallels to the well known Baum-Welch EM algorithm for off-line estimation of HMM parameters. Almost sure convergence results and convergence rates results are established using martingale convergence results, the Kronecker lemma and an ODE approach. This inspires the proposal of globally convergent parameter estimators for partially observed linear systems and hybrid linear systems. Almost sure convergence results are established using martingale convergence results, the Kronecker lemma and an ODE approach. Finally, as a contribution towards applications, optimal HMM filters are developed for demodulation of differentially encoded transmission systems and a decision feedback equalizer is proposed.
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Bulla, Jan. "Application of Hidden Markov and Hidden Semi-Markov Models to Financial Time Series." Doctoral thesis, [S.l. : s.n.], 2006. http://swbplus.bsz-bw.de/bsz260867136inh.pdf.
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Puigcerver, I. Pérez Joan. "A Probabilistic Formulation of Keyword Spotting." Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/116834.
Full textAbstract:
[ES] La detección de palabras clave (Keyword Spotting, en inglés), aplicada a documentos de texto manuscrito, tiene como objetivo recuperar los documentos, o partes de ellos, que sean relevantes para una cierta consulta (query, en inglés), indicada por el usuario, entre una gran colección de documentos. La temática ha recogido un gran interés en los últimos 20 años entre investigadores en Reconocimiento de Formas (Pattern Recognition), así como bibliotecas y archivos digitales.
Esta tesis, en primer lugar, define el objetivo de la detección de palabras clave a partir de una perspectiva basada en la Teoría de la Decisión y una formulación probabilística adecuada. Más concretamente, la detección de palabras clave se presenta como un caso particular de Recuperación de la Información (Information Retrieval), donde el contenido de los documentos es desconocido, pero puede ser modelado mediante una distribución de probabilidad. Además, la tesis también demuestra que, bajo las distribuciones de probabilidad correctas, el marco de trabajo desarrollada conduce a la solución óptima del problema, según múltiples medidas de evaluación utilizadas tradicionalmente en el campo.
Más tarde, se utilizan distintos modelos estadísticos para representar las distribuciones necesarias: Redes Neuronales Recurrentes o Modelos Ocultos de Markov. Los parámetros de estos son estimados a partir de datos de entrenamiento, y las respectivas distribuciones son representadas mediante Transductores de Estados Finitos con Pesos (Weighted Finite State Transducers).
Con el objetivo de hacer que el marco de trabajo sea práctico en grandes colecciones de documentos, se presentan distintos algoritmos para construir índices de palabras a partir de modelos probabilísticos, basados tanto en un léxico cerrado como abierto. Estos índices son muy similares a los utilizados por los motores de búsqueda tradicionales.
Además, se estudia la relación que hay entre la formulación probabilística presentada y otros métodos de gran influencia en el campo de la detección de palabras clave, destacando cuáles son las limitaciones de los segundos.
Finalmente, todas la aportaciones se evalúan de forma experimental, no sólo utilizando pruebas académicas estándar, sino también en colecciones con decenas de miles de páginas provenientes de manuscritos históricos. Los resultados muestran que el marco de trabajo presentado permite construir sistemas de detección de palabras clave muy rápidos y precisos, con una sólida base teórica.[CAT] La detecció de paraules clau (Keyword Spotting, en anglès), aplicada a documents de text manuscrit, té com a objectiu recuperar els documents, o parts d'ells, que siguen rellevants per a una certa consulta (query, en anglès), indicada per l'usuari, dintre d'una gran col·lecció de documents. La temàtica ha recollit un gran interés en els últims 20 anys entre investigadors en Reconeixement de Formes (Pattern Recognition), així com biblioteques i arxius digitals. Aquesta tesi defineix l'objectiu de la detecció de paraules claus a partir d'una perspectiva basada en la Teoria de la Decisió i una formulació probabilística adequada. Més concretament, la detecció de paraules clau es presenta com un cas concret de Recuperació de la Informació (Information Retrieval), on el contingut dels documents és desconegut, però pot ser modelat mitjançant una distribució de probabilitat. A més, la tesi també demostra que, sota les distribucions de probabilitat correctes, el marc de treball desenvolupat condueix a la solució òptima del problema, segons diverses mesures d'avaluació utilitzades tradicionalment en el camp. Després, diferents models estadístics s'utilitzen per representar les distribucions necessàries: Xarxes Neuronal Recurrents i Models Ocults de Markov. Els paràmetres d'aquests són estimats a partir de dades d'entrenament, i les corresponents distribucions són representades mitjançant Transductors d'Estats Finits amb Pesos (Weighted Finite State Transducers). Amb l'objectiu de fer el marc de treball útil per a grans col·leccions de documents, es presenten distints algorismes per construir índexs de paraules a partir dels models probabilístics, tan basats en un lèxic tancat com en un obert. Aquests índexs són molt semblants als utilitzats per motors de cerca tradicionals. A més a més, s'estudia la relació que hi ha entre la formulació probabilística presentada i altres mètodes de gran influència en el camp de la detecció de paraules clau, destacant algunes limitacions dels segons. Finalment, totes les aportacions s'avaluen de forma experimental, no sols utilitzant proves acadèmics estàndard, sinó també en col·leccions amb desenes de milers de pàgines provinents de manuscrits històrics. Els resultats mostren que el marc de treball presentat permet construir sistemes de detecció de paraules clau molt acurats i ràpids, amb una sòlida base teòrica.
[EN] Keyword Spotting, applied to handwritten text documents, aims to retrieve the documents, or parts of them, that are relevant for a query, given by the user, within a large collection of documents. The topic has gained a large interest in the last 20 years among Pattern Recognition researchers, as well as digital libraries and archives. This thesis, first defines the goal of Keyword Spotting from a Decision Theory perspective. Then, the problem is tackled following a probabilistic formulation. More precisely, Keyword Spotting is presented as a particular instance of Information Retrieval, where the content of the documents is unknown, but can be modeled by a probability distribution. In addition, the thesis also proves that, under the correct probability distributions, the framework provides the optimal solution, under many of the evaluation measures traditionally used in the field. Later, different statistical models are used to represent the probability distribution over the content of the documents. These models, Hidden Markov Models or Recurrent Neural Networks, are estimated from training data, and the corresponding distributions over the transcripts of the images can be efficiently represented using Weighted Finite State Transducers. In order to make the framework practical for large collections of documents, this thesis presents several algorithms to build probabilistic word indexes, using both lexicon-based and lexicon-free models. These indexes are very similar to the ones used by traditional search engines. Furthermore, we study the relationship between the presented formulation and other seminal approaches in the field of Keyword Spotting, highlighting some limitations of the latter. Finally, all the contributions are evaluated experimentally, not only on standard academic benchmarks, but also on collections including tens of thousands of pages of historical manuscripts. The results show that the proposed framework and algorithms allow to build very accurate and very fast Keyword Spotting systems, with a solid underlying theory.
Puigcerver I Pérez, J. (2018). A Probabilistic Formulation of Keyword Spotting [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/116834
TESIS
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Farges, Eric P. "An analysis-synthesis hidden Markov model of speech." Diss., Georgia Institute of Technology, 1987. http://hdl.handle.net/1853/14775.
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Dey, Arkajit. "Hidden Markov model analysis of subcellular particle trajectories." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66307.
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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 71-73).
How do proteins, vesicles, or other particles within a cell move? Do they diffuse randomly or ow in a particular direction? Understanding how subcellular particles move in a cell will reveal fundamental principles of cell biology and biochemistry, and is a necessary prerequisite to synthetically engineering such processes. We investigate the application of several variants of hidden Markov models (HMMs) to analyzing the trajectories of such particles. And we compare the performance of our proposed algorithms with traditional approaches that involve fitting a mean square displacement (MSD) curve calculated from the particle trajectories. Our HMM algorithms are shown to be more accurate than existing MSD algorithms for heterogeneous trajectories which switch between multiple phases of motion.
by Arkajit Dey.
M.Eng.
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Le, Riche Pierre (Pierre Jacques). "Handwritten signature verification : a hidden Markov model approach." Thesis, Stellenbosch : Stellenbosch University, 2000. http://hdl.handle.net/10019.1/51784.
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Thesis (MEng)--University of Stellenbosch, 2000.ENGLISH ABSTRACT: Handwritten signature verification (HSV) is the process through which handwritten signatures are analysed in an attempt to determine whether the person who made the signature is who he claims to be. Banks and other financial institutions lose billions of rands annually to cheque fraud and other crimes that are preventable with the aid of good signature verification techniques. Unfortunately, the volume of cheques that are processed precludes a thorough HSV process done in the traditional manner by human operators. It is the aim of this research to investigate new methods to compare signatures automatically, to eventually speed up the HSV process and improve on the accuracy of existing systems. The new technology that is investigated is the use of the so-called hidden Markov models (HMMs). It is only quite recently that the computing power has become commonly available to make the real-time use of HMMs in pattern recognition a possibility. Two demonstration programs, SigGrab and Securitlheque, have been developed that make use of this technology, and show excellent improvements over other techniques and competing products. HSV accuracies in excess of99% can be attained.
AFRIKAANSE OPSOMMING: Handgeskrewe handtekening verifikasie (HHV) is die proses waardeur handgeskrewe handtekeninge ondersoek word in 'n poging om te bevestig of die persoon wat die handtekening gemaak het werklik is wie hy voorgee om te wees. Banke en ander finansiele instansies verloor jaarliks biljoene rande aan tjekbedrog en ander misdrywe wat voorkom sou kon word indien goeie metodes van handtekening verifikasie daargestel kon word. Ongelukkig is die volume van tjeks wat hanteer word so groot, dat tradisionele HHV deur menslike operateurs 'n onbegonne taak is. Dit is die doel van hierdie navorsmg om nuwe metodes te ondersoek om handtekeninge outomaties te kan vergelyk en so die HHV proses te bespoedig en ook te verbeter op die akkuraatheid van bestaande stelsels. Die nuwe tegnologie wat ondersoek is is die gebruik van die sogenaamde verskuilde Markov modelle (VMMs). Dit is eers redelik onlangs dat die rekenaar verwerkingskrag algemeen beskikbaar geraak het om die intydse gebruik van VMMs in patroonherkenning prakties moontlik te maak. Twee demonstrasieprogramme, SigGrab en SecuriCheque, is ontwikkel wat gebruik maak van hierdie tegnologie en toon uitstekende verbeterings teenoor ander tegnieke en kompeterende produkte. 'n Akkuraatheid van 99% of hoer word tipies verkry.
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Seneviratne, Vidura Priyaranjana. "The hidden vector state language model." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613351.
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Alneberg, Johannes. "Movement of a prawn: a Hidden Markov Model approach." Thesis, Uppsala universitet, Analys och tillämpad matematik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-155994.
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Dawson, Colin Reimer, and Colin Reimer Dawson. "HaMMLeT: An Infinite Hidden Markov Model with Local Transitions." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/626170.
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In classical mixture modeling, each data point is modeled as arising i.i.d. (typically) from a weighted sum of probability distributions. When data arises from different sources that may not give rise to the same mixture distribution, a hierarchical model can allow the source contexts (e.g., documents, sub-populations) to share components while assigning different weights across them (while perhaps coupling the weights to "borrow strength" across contexts). The Dirichlet Process (DP) Mixture Model (e.g., Rasmussen (2000)) is a Bayesian approach to mixture modeling which models the data as arising from a countably infinite number of components: the Dirichlet Process provides a prior on the mixture weights that guards against overfitting. The Hierarchical Dirichlet Process (HDP) Mixture Model (Teh et al., 2006) employs a separate DP Mixture Model for each context, but couples the weights across contexts. This coupling is critical to ensure that mixture components are reused across contexts.
An important application of HDPs is to time series models, in particular Hidden Markov Models (HMMs), where the HDP can be used as a prior on a doubly infinite transition matrix for the latent Markov chain, giving rise to the HDP-HMM (first developed, as the "Infinite HMM", by Beal et al. (2001), and subsequently shown to be a case of an HDP by Teh et al. (2006)). There, the hierarchy is over rows of the transition matrix, and the distributions across rows are coupled through a top-level Dirichlet Process.
In the first part of the dissertation, I present a formal overview of Mixture Models and Hidden Markov Models. I then turn to a discussion of Dirichlet Processes and their various representations, as well as associated schemes for tackling the problem of doing approximate inference over an infinitely flexible model with finite computa- tional resources. I will then turn to the Hierarchical Dirichlet Process (HDP) and its application to an infinite state Hidden Markov Model, the HDP-HMM.
These models have been widely adopted in Bayesian statistics and machine learning. However, a limitation of the vanilla HDP is that it offers no mechanism to model correlations between mixture components across contexts. This is limiting in many applications, including topic modeling, where we expect certain components to occur or not occur together. In the HMM setting, we might expect certain states to exhibit similar incoming and outgoing transition probabilities; that is, for certain rows and columns of the transition matrix to be correlated. In particular, we might expect pairs of states that are "similar" in some way to transition frequently to each other. The HDP-HMM offers no mechanism to model this similarity structure.
The central contribution of the dissertation is a novel generalization of the HDP- HMM which I call the Hierarchical Dirichlet Process Hidden Markov Model With Local Transitions (HDP-HMM-LT, or HaMMLeT for short), which allows for correlations between rows and columns of the transition matrix by assigning each state a location in a latent similarity space and promoting transitions between states that are near each other. I present a Gibbs sampling scheme for inference in this model, employing auxiliary variables to simplify the relevant conditional distributions, which have a natural interpretation after re-casting the discrete time Markov chain as a continuous time Markov Jump Process where holding times are integrated out, and where some jump attempts "fail". I refer to this novel representation as the Markov Process With Failed Jumps. I test this model on several synthetic and real data sets, showing that for data where transitions between similar states are more common, the HaMMLeT model more effectively finds the latent time series structure underlying the observations.
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TALARICO, ERICK COSTA E. SILVA. "SEISMIC TO FACIES INVERSION USING CONVOLVED HIDDEN MARKOV MODEL." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=36004@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROA indústria de óleo e gás utiliza a sísmica para investigar a distribuição de tipos de rocha (facies) em subsuperfície. Por outro lado, apesar de seu corriqueiro uso em geociências, medidas sísmicas costumam ser ruidosas, e a inversão do dado sísmico para a distribuição de facies é um problema mal posto. Por esta razão, diversos autores estudam esta inversão sob o ponto de vista probabilístico, para ao menos estimar as incertezas da solução do problema inverso. O objetivo da presente dissertação é desenvolver método quantitativo para estimar a probabilidade de reservatório com hidrocarboneto, dado um traço sísmico de reflexão, integrando modelagem sísmica direta, e conhecimento geológico a priori. Utiliza-se, um dos métodos mais recentes para resolver o problema inverso: Modelo de Markov Oculto com Efeito Convolucional (mais especificamente, a Aproximação por Projeção de (1)). É demonstrado que o método pode ser reformulado em termos do Modelo de Markov Oculto (MMO) ordinário. A teoria de sísmica de AVA é apresentada, e usada conjuntamente com MMO com Efeito Convolucional para resolver a inversão de sísmica para facies. A técnica de inversão é avaliada usando-se medidas difundidas em Aprendizado de Máquina, em um conjunto de experimentos variados e realistas. Apresenta-se uma técnica para medir a capacidade do algoritmo em estimar valores confiáveis de probabilidade. Pelos testes realizados a aproximação por projeção apresenta distorções de probabilidade inferiores a 5 por cento, tornando-a uma técnica útil para a indústria de óleo e gás.
Oil and Gas Industry uses seismic data in order to unravel the distribution of rock types (facies) in the subsurface. But, despite its widespread use, seismic data is noisy and the inversion from seismic data to the underlying rock distribution is an ill-posed problem. For this reason, many authors have studied the topic in a probabilistic formulation, in order to provide uncertainty estimations about the solution of the inversion problem. The objective of the present thesis is to develop a quantitative method to estimate the probability of hydrocarbon bearing reservoir, given a seismic reflection profile, and, to integrate geological prior knowledge with geophysical forward modelling. One of the newest methods for facies inversion is used: Convolved Hidden Markov Model (more specifically the Projection Approximation from (1)). It is demonstrated how Convolved HMM can be reformulated as an ordinary Hidden Markov Model problem (which models geological prior knowledge). Seismic AVA theory is introduced, and used with Convolved HMM theory to solve the seismic to facies problem. The performance of the inversion technique is measured with common machine learning scores, in a broad set of realistic experiments. The technique capability of estimating reliable probabilities is quantified, and it is shown to present distortions smaller than 5 percent. As a conclusion, the studied Projection Approximation is applicable for risk management in Oil and Gas applications, which integrates geological and geophysical knowledge.
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Wynne-Jones, Michael. "Model building in neural networks with hidden Markov models." Thesis, University of Edinburgh, 1994. http://hdl.handle.net/1842/284.
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This thesis concerns the automatic generation of architectures for neural networks and other pattern recognition models comprising many elements of the same type. The requirement for such models, with automatically determined topology and connectivity, arises from two needs. The first is the need to develop commercial applications of the technology without resorting to laborious trial and error with different network sizes; the second is the need, in large and complex pattern processing applications such as speech recognition, to optimise the allocation of computing resources for problem solving. The state of the art in adaptive architectures is reviewed, and a mechanism is proposed for adding new processing elements to models. The scheme is developed in the context of multi-layer perceptron networks, and is linked to the best network-pruning mechanism available using a numerical criterion with construction required at one extreme and pruning at the other. The construction mechanism does not work in the multi-layer perceptron for which it was developed, owing to the long-range effects occurring in many applications of these networks. It works demonstrably well in density estimation models based on Gaussian mixtures, which are of the same family as the increasingly popular radial basis function networks. The construction mechanism is applied to the initialization of the density estimators embedded in the states of a hidden Markov model for speaker-independent speech recognition, where it offers a considerable increase in recogniser performance, provided cross-validation is used to prevent over-training.
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Jiang, Zuliang. "Hidden Markov Model with Binned Duration and Its Application." ScholarWorks@UNO, 2010. http://scholarworks.uno.edu/td/1108.
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Hidden Markov models (HMM) have been widely used in various applications such as speech processing and bioinformatics. However, the standard hidden Markov model requires state occupancy durations to be geometrically distributed, which can be inappropriate in some real-world applications where the distributions on state intervals deviate signi cantly from the geometric distribution, such as multi-modal distributions and heavy-tailed distributions. The hidden Markov model with duration (HMMD) avoids this limitation by explicitly incor- porating the appropriate state duration distribution, at the price of signi cant computational expense. As a result, the applications of HMMD are still quited limited. In this work, we present a new algorithm - Hidden Markov Model with Binned Duration (HMMBD), whose result shows no loss of accuracy compared to the HMMD decoding performance and a com- putational expense that only diers from the much simpler and faster HMM decoding by a constant factor. More precisely, we further improve the computational complexity of HMMD from (TNN +TND) to (TNN +TND ), where TNN stands for the computational com- plexity of the HMM, D is the max duration value allowed and can be very large and D generally could be a small constant value.
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Wilhelmsson, Anna, and Sofia Bedoire. "Driving Behavior Prediction by Training a Hidden Markov Model." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291656.
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Introducing automated vehicles in to traffic withhuman drivers, human behavior prediction is essential to obtainoperation safety. In this study, a human behavior estimationmodel has been developed. The estimations are based on aHidden Markov Model (HMM) using observations to determinethe driving style of surrounding vehicles. The model is trainedusing two different methods: Baum Welch training and Viterbitraining to improve the performance. Both training methods areevaluated by looking at time complexity and convergence. Themodel is implemented with and without training and tested fordifferent driving styles. Results show that training is essentialfor accurate human behavior prediction. Viterbi training is fasterbut more noise sensitive compared to Baum Welch training. Also,Viterbi training produces good results if training data reflects oncurrently observed driver, which is not always the case. BaumWelch training is more robust in such situations. Lastly, BaumWelch training is recommended to obtain operation safety whenintroducing automated vehicles into traffic.N ̈ar automatiserade fordon introduceras itrafiken och beh ̈over interagera med m ̈anskliga f ̈orare ̈ar det vik-tigt att kunna f ̈orutsp ̊a m ̈anskligt beteende. Detta f ̈or att kunnaerh ̊alla en s ̈akrare trafiksituation. I denna studie har en modellsom estimerar m ̈anskligt beteende utvecklats. Estimeringarna ̈ar baserade p ̊a en Hidden Markov Model d ̈ar observationeranv ̈ands f ̈or att best ̈amma k ̈orstil hos omgivande fordon itrafiken. Modellen tr ̈anas med tv ̊a olika metoder: Baum Welchtr ̈aning och Viterbi tr ̈aning f ̈or att f ̈orb ̈attra modellens prestanda.Tr ̈aningsmetoderna utv ̈arderas sedan genom att analysera derastidskomplexitet och konvergens. Modellen ̈ar implementerad medoch utan tr ̈aning och testad f ̈or olika k ̈orstilar. Erh ̊allna resultatvisar att tr ̈aning ̈ar viktigt f ̈or att kunna f ̈orutsp ̊a m ̈anskligtbeteende korrekt. Viterbi tr ̈aning ̈ar snabbare men mer k ̈ansligf ̈or brus i j ̈amf ̈orelse med Baum Welch tr ̈aning. Viterbi tr ̈aningger ̈aven en bra estimering i de fall d ̊a observerad tr ̈aningsdataavspeglar f ̈orarens k ̈orstil, vilket inte alltid ̈ar fallet. BaumWelch tr ̈aning ̈ar mer robust i s ̊adana situationer. Slutligenrekommenderas en estimeringsmodell implementerad med BaumWelch tr ̈aning f ̈or att erh ̊alla en s ̈aker k ̈orning d ̊a automatiseradefordon introduceras i trafiken
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm
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Chan, Kin Wah. "Pruning of hidden Markov model with optimal brain surgeon /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202003%20CHAN.
Full textAbstract:
Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.Includes bibliographical references (leaves 72-76). Also available in electronic version. Access restricted to campus users.
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Lindberg, David Seaman III. "Enhancing Individualized Instruction through Hidden Markov Models." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1405350981.
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Langrock, Roland. "On some special-purpose hidden Markov models." Doctoral thesis, 2011. http://hdl.handle.net/11858/00-1735-0000-0006-B6AF-E.
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Bartnik, Grant. "On Improved Generalization of 5-State Hidden Markov Model-based Internet Traffic Classifiers." Thesis, 2013. http://hdl.handle.net/10214/7237.
Full textAbstract:
The multitude of services delivered over the Internet would have been difficult to fathom 40 years ago when much of the initial design was being undertaken. As a consequence, the resulting architecture did not make provisions for differentiating between, and managing the potentially conflicting requirements of different types of services such as real-time voice communication and peer-to-peer file sharing. This shortcoming has resulted in a situation whereby services with conflicting requirements often interfere with each other and ultimately decrease the effectiveness of the Internet as an enabler of new and transformative services. The ability to passively identify different types of Internet traffic then would address this shortcoming and enable effective management of conflicting types of services, in addition to facilitating a better understanding of how the Internet is used in general. Recent attempts at developing such techniques have shown promising results in simulation environments but perform considerably worse when deployed into real-world scenarios. One possible reason for this descrepancy can be attributed to the implicit assumption shared by recent approaches regarding the degree of similarity between the many networks which comprise the Internet. This thesis quantifies the degradation in performance which can be expected when such an assumption is violated as well as demonstrating alternative classification techniques which are less sensitive to such violations.
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Harker, William Gordon. "Real time furnace froth state detection using Hidden Markov Models." Thesis, 2013. http://hdl.handle.net/10539/12828.
Full textAbstract:
In this dissertation the feasibility of developing a soft sensor utilising Hidden Markov
Models (HMM) was evaluated. Specifically, this methodology was tested for use as a
soft sensor to detect furnace froths in a real time environment.
Initially, a review of Hidden Markov Models was undertaken to gain an understanding
of the mathematics and algorithms associated with HMM's. A simple HMM example
was constructed to highlight practical problems associated with HMM's. One such
problem identified was that HMM's are unsuitable for real time use without
modification.
Potential modifications were then researched to improve the real time performance of
the HMM. This research yielded a real time variant of the HMM Viterbi algorithm,
labelled Real Time Viterbi (RTV), as a potential modification. In addition a new hybrid
algorithm, labelled the Hidden Markov Model Fixed State Test (HMM FST), was
developed by the Author.
Comparative studies of the respective real time performances of the RTV and HMM
FST algorithms concluded that the HMM FST algorithm was the most suitable for use
in the real world application.
A final HMM FST real time algorithm was developed which incorporated the use of KMeans
Clustering techniques. Data files, consisting of electrode positions from real
furnace froths, were then replayed into the HMM FST algorithm to evaluate its
performance. Four scenarios, incorporating different HMM FST tuning parameters,
were then executed to determine the impact of the model parameters on its froth
detection ability and false positive response. A final tuning set was recommended for
the HMM FST Furnace Froth Detector.
This research proved that this approach can be used as a practical soft sensor to
detect furnace froths in electric arc furnaces with any structural or electrode
configuration. The HMM FST model could be tuned to various levels of sensitivity
and was found to generate low false positives due to its treatment of plant sensors as
a collective.
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Shue, Louis. "On performance analysis of state estimators for hidden Markov models." Phd thesis, 1999. http://hdl.handle.net/1885/147620.
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LIN, I.-HSIN, and 林宜欣. "Applying Data Clustering on Determining the Number of Hidden States of Hidden Markov Model." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/45k2n6.
Full textAbstract:
碩士國立臺灣科技大學
工業管理系
105
This research proposes the data analysis method for determining the number of hidden states of Hidden Markov Model (HMM). Although HMM has been widely used for pattern recognition, handwriting character recognition, stock prediction, and preventive maintenance and so on. However, there was only a few research has been conducted on the determination of the number of hidden states. Based on the literature review, Akaike Information Criteria (AIC), and Bayesian Information Criteria (BIC) were applied to search the number of hidden states by maximizing the likelihood of each model. In this research, the data clustering method is proposed to study the hidden patterns among the data which will be trained in HMM. The multiple clustering validation measures with computational time are included in the decision making of the number of hidden states. The Pareto Optimal Front is utilized to deal with multi-objective problem based on the multiple criterion. The experimental results conducted on fours datasets regarding preventive maintenance showed that the proposed method is able to find the suitable number of hidden states which also optimize the efficiency of HMM.
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Chen, Chien-Jen, and 陳建仁. "Combining Hidden Markov Model with Ensemble Learning to Predict Hidden States and Conduct Stochastic Simulation." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/mhh87z.
Full textAbstract:
碩士國立交通大學
工業工程與管理系所
106
Taiwan’s semiconductor industry, optoelectronics industry, computers and peripheral equipment industry play an important role in the world. Additionally, the rapid development of Artificial Intelligence (AI) and Internet of Things (IoT) have also driven the growth of these industries. Although the overall industry is growing up, there is a significant gap between the firms within the industry. Therefore, this study focuses on those companies which revenues go up and down. First, Hidden Markov Model (HMM) is used to explore the company’s hidden states. Without loss of generality, three hidden states, such as healthy, risky, and sick are used in this thesis. In particular, the hidden states are linked into measurable variables, namely, NPBT (net profit before tax), EPS (earning per share), and ROE (return on equity). In addition, 19 representative independent variables used to predict hidden states and conduct stochastic simulation. This study use ensemble learning to identify the key performance indicators (KPIs) of hidden states and then uses Bayesian Belief Network (BBN) to conduct stochastic simulations. Based on the presented framework, the impact of the abovementioned KPI on the hidden state and NPBT can be quantitatively measured. Finally, management implications are provided to improve the company’s operational efficiency.
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Bruce-Doust, Riley. "Forgetting properties of finite-state reciprocal processes." Thesis, 2017. http://hdl.handle.net/2440/113380.
Full textAbstract:
Reciprocal chains (RC) are a class of discrete-index, finite-state stochastic process
having the non-causal generalisation of the Markov property, where rather than the
future being conditionally independent of the past given the present, intervals are
conditionally independent of their complement given their endpoints. RCs are more
powerful models than Markov chains (MC) but are n times more complex to process
with their associated smoothing algorithm for number of states n, that is O(n³T) for
fixed interval smoothing an interval of length T. In this thesis it was established that
RCs have a forgetting property - geometric decay in dependence between separated
variables: it was found that the dependence matrix between two variables in an RC
has a form expressible in terms of element-wise product of matrix products. The theory
of forgetting in matrix products using Birkhoff's contraction coefficient, was extended
to this case. It was shown that because MCs are a special case of RCs, arising under
particular boundary conditions, forgetting property means the distributions of the
RC that are far from the boundary condition are well approximated by those of MC
models. It was shown that the forgetting property extends to the RC fixed interval
smoothing algorithm so that close approximation occurs by estimates from the MC
interval smoother for variables far from the boundary. These results would imply
that RCs were not computationally efficient models for intervals that are long with
respect to the forgetting rate, however an approximate interval smoothing algorithm
was developed for RCs which is a modified form of the MC algorithm (the forward-backward
algorithm) and is of comparable complexity to it. The forgetting theory was
used to bound the error in the approximation, which is small on the long intervals for
which the RC was inefficient for exact estimation.Thesis (M.Phil) -- University of Adelaide, School of Electrical and Electronic Engineering, 2017