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Published: 1 July 2022

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1

Devilliers, Esther. "Modélisation micro-économétrique des choix de pratiques de production et des utilisations d'intrants chimiques des agriculteurs : une approche par les fonctions de production latentes." Thesis, Rennes, Agrocampus Ouest, 2021. http://www.theses.fr/2021NSARE058.

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La notion d’itinéraire technique est une notion agronomique qui nous permet d’appréhender l’imbrication entre les rendements objectifs et les niveaux d’utilisation d’intrants associés. Dès lors, on peut admettre qu’à différents types d’itinéraires techniques correspondent différentes fonctions de production. Modéliser ces différentes fonctions est une des clés pour mieux comprendre la dépendance de certaines pratiques culturales aux pesticides et de ce fait constitue un enjeu majeur pour concevoir les futures politiques publiques.Intégrer cette notion d’itinéraire technique nécessite de tenir compte de l’interdépendance entre les choix de ces pratiques, leur rendement et les utilisations associées. Pour ce faire, on considère des modèles de changement de régime endogène qui permettent de contrôler des biais de sélection. Lorsque ces pratiques sont inobservées, on définit la séquence de choix comme processus Markovien.Le modèle résultant nous permet de recouvrir les pratiques culturales, leurs niveaux de rendement et d’utilisation d’intrants ainsi que la dynamique de choix des dites pratiques. Lorsque ces pratiques sont observées, on décide de considérer un modèle primal afin de pouvoir vérifier le rôle différencié des pesticides et évaluer l’effet des politiques publiques conjointement sur les rendements et les niveaux d’utilisation d’intrants chimiques.En bref, cette thèse vise à donner des outils pour évaluer au mieux les effets des politiques agro-environnementales sur les utilisations de pesticides, les rendements et mes choix de pratiques culturales des agriculteurs
Cropping management practices is an agronomic notion grasping the interdependence between targeted yield and input use levels. Subsequently, one can legitimately assume that different cropping management practices are associated to different production functions. To better understand pesticide dependence – a key point to encourage more sustainable practices – one have to consider modelling cropping management practices specific production functions.Because of the inherent interdependence between those practices and their associeted yield and input use levels, we need to consider endogenous regime switching models.When unobserved, the sequence of cropping management practices choices is considered as a Markovian process. From this modelling framework we can derive the cropping management choices, their dynamics, their associated yield and input use levels. When observed, we consider primal production functions to see how yield responds differently to input uses based on the different cropping management practices. Thus, we can assess jointly the effect of a public policy on input use and yield levels.In a nutshell, in this PhD we are aiming at giving some tools to evaluate the differentiated effect of agri-environmental public policies on production choies and on the associated yield and input use levels
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2

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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3

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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4

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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5

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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6

Tillman, Måns. "On-Line Market Microstructure Prediction Using Hidden Markov Models." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208312.

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Over the last decades, financial markets have undergone dramatic changes. With the advent of the arbitrage pricing theory, along with new technology, markets have become more efficient. In particular, the new high-frequency markets, with algorithmic trading operating on micro-second level, make it possible to translate ”information” into price almost instantaneously. Such phenomena are studied in the field of market microstructure theory, which aims to explain and predict them. In this thesis, we model the dynamics of high frequency markets using non-linear hidden Markov models (HMMs). Such models feature an intuitive separation between observations and dynamics, and are therefore highly convenient tools in financial settings, where they allow a precise application of domain knowledge. HMMs can be formulated based on only a few parameters, yet their inherently dynamic nature can be used to capture well-known intra-day seasonality effects that many other models fail to explain. Due to recent breakthroughs in Monte Carlo methods, HMMs can now be efficiently estimated in real-time. In this thesis, we develop a holistic framework for performing both real-time inference and learning of HMMs, by combining several particle-based methods. Within this framework, we also provide methods for making accurate predictions from the model, as well as methods for assessing the model itself. In this framework, a sequential Monte Carlo bootstrap filter is adopted to make on-line inference and predictions. Coupled with a backward smoothing filter, this provides a forward filtering/backward smoothing scheme. This is then used in the sequential Monte Carlo expectation-maximization algorithm for finding the optimal hyper-parameters for the model. To design an HMM specifically for capturing information translation, we adopt the observable volume imbalance into a dynamic setting. Volume imbalance has previously been used in market microstructure theory to study, for example, price impact. Through careful selection of key model assumptions, we define a slightly modified observable as a process that we call scaled volume imbalance. The outcomes of this process retain the key features of volume imbalance (that is, its relationship to price impact and information), and allows an efficient evaluation of the framework, while providing a promising platform for future studies. This is demonstrated through a test on actual financial trading data, where we obtain high-performance predictions. Our results demonstrate that the proposed framework can successfully be applied to the field of market microstructure.
Under de senaste decennierna har det gjorts stora framsteg inom finansiell teori för kapitalmarknader. Formuleringen av arbitrageteori medförde möjligheten att konsekvent kunna prissätta finansiella instrument. Men i en tid då högfrekvenshandel numera är standard, har omsättningen av information i pris börjat ske i allt snabbare takt. För att studera dessa fenomen; prispåverkan och informationsomsättning, har mikrostrukturteorin vuxit fram. I den här uppsatsen studerar vi mikrostruktur med hjälp av en dynamisk modell. Historiskt sett har mikrostrukturteorin fokuserat på statiska modeller men med hjälp av icke-linjära dolda Markovmodeller (HMM:er) utökar vi detta till den dynamiska domänen. HMM:er kommer med en naturlig uppdelning mellan observation och dynamik, och är utformade på ett sådant sätt att vi kan dra nytta av domänspecifik kunskap. Genom att formulera lämpliga nyckelantaganden baserade på traditionell mikrostrukturteori specificerar vi en modell—med endast ett fåtal parametrar—som klarar av att beskriva de välkända säsongsbeteenden som statiska modeller inte klarar av. Tack vare nya genombrott inom Monte Carlo-metoder finns det nu kraftfulla verktyg att tillgå för att utföra optimal filtrering med HMM:er i realtid. Vi applicerar ett så kallat bootstrap filter för att sekventiellt filtrera fram tillståndet för modellen och prediktera framtida tillstånd. Tillsammans med tekniken backward smoothing estimerar vi den posteriora simultana fördelningen för varje handelsdag. Denna används sedan för statistisk inlärning av våra hyperparametrar via en sekventiell Monte Carlo Expectation Maximization-algoritm. För att formulera en modell som beskriver omsättningen av information, väljer vi att utgå ifrån volume imbalance, som ofta används för att studera prispåverkan. Vi definierar den relaterade observerbara storheten scaled volume imbalance som syftar till att bibehålla kopplingen till prispåverkan men även går att modellera med en dynamisk process som passar in i ramverket för HMM:er. Vi visar även hur man inom detta ramverk kan utvärdera HMM:er i allmänhet, samt genomför denna analys för vår modell i synnerhet. Modellen testas mot finansiell handelsdata för både terminskontrakt och aktier och visar i bägge fall god predikteringsförmåga.
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7

Hofer, Gregor Otto. "Speech-driven animation using multi-modal hidden Markov models." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/3786.

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The main objective of this thesis was the synthesis of speech synchronised motion, in particular head motion. The hypothesis that head motion can be estimated from the speech signal was confirmed. In order to achieve satisfactory results, a motion capture data base was recorded, a definition of head motion in terms of articulation was discovered, a continuous stream mapping procedure was developed, and finally the synthesis was evaluated. Based on previous research into non-verbal behaviour basic types of head motion were invented that could function as modelling units. The stream mapping method investigated in this thesis is based on Hidden Markov Models (HMMs), which employ modelling units to map between continuous signals. The objective evaluation of the modelling parameters confirmed that head motion types could be predicted from the speech signal with an accuracy above chance, close to 70%. Furthermore, a special type ofHMMcalled trajectoryHMMwas used because it enables synthesis of continuous output. However head motion is a stochastic process therefore the trajectory HMM was further extended to allow for non-deterministic output. Finally the resulting head motion synthesis was perceptually evaluated. The effects of the “uncanny valley” were also considered in the evaluation, confirming that rendering quality has an influence on our judgement of movement of virtual characters. In conclusion a general method for synthesising speech-synchronised behaviour was invented that can applied to a whole range of behaviours.
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8

McKee, Bill Frederick. "Optimal hidden Markov models." Thesis, University of Plymouth, 1999. http://hdl.handle.net/10026.1/1698.

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In contrast with training algorithms such as Baum-Welch, which produce solutions that are a local optimum of the objective function, this thesis describes the attempt to develop a training algorithm which delivers the global optimum Discrete ICdden Markov Model for a given training sequence. A total of four different methods of attack upon the problem are presented. First, after building the necessary analytical tools, the thesis presents a direct, calculus-based assault featuring Matrix Derivatives. Next, the dual analytic approach known as Geometric Programming is examined and then adapted to the task. After that, a hill-climbing formula is developed and applied. These first three methods reveal a number of interesting and useful insights into the problem. However, it is the fourth method which produces an algorithm that is then used for direct comparison vAth the Baum-Welch algorithm: examples of global optima are collected, examined for common features and patterns, and then a rule is induced. The resulting rule is implemented in *C' and tested against a battery of Baum-Welch based programs. In the limited range of tests carried out to date, the models produced by the new algorithm yield optima which have not been surpassed by (and are typically much better than) the Baum-Welch models. However, far more analysis and testing is required and in its current form the algorithm is not fast enough for realistic application.
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9

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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10

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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11

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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Lystig, Theodore C. "Evaluation of hidden Markov models /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/9597.

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Van, Gael Jurgen. "Bayesian nonparametric hidden Markov models." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610196.

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14

Ali, Asif. "Voice query-by-example for resource-limited languages using an ergodic hidden Markov model of speech." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50363.

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An ergodic hidden Markov model (EHMM) can be useful in extracting underlying structure embedded in connected speech without the need for a time-aligned transcribed corpus. In this research, we present a query-by-example (QbE) spoken term detection system based on an ergodic hidden Markov model of speech. An EHMM-based representation of speech is not invariant to speaker-dependent variations due to the unsupervised nature of the training. Consequently, a single phoneme may be mapped to a number of EHMM states. The effects of speaker-dependent and context-induced variation in speech on its EHMM-based representation have been studied and used to devise schemes to minimize these variations. Speaker-invariance can be introduced into the system by identifying states with similar perceptual characteristics. In this research, two unsupervised clustering schemes have been proposed to identify perceptually similar states in an EHMM. A search framework, consisting of a graphical keyword modeling scheme and a modified Viterbi algorithm, has also been implemented. An EHMM-based QbE system has been compared to the state-of-the-art and has been demonstrated to have higher precisions than those based on static clustering schemes.
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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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17

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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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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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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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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21

Wang, Yijie Dylan. "Hidden Markov model with application in cell adhesion experiment and Bayesian cubic splines in computer experiments." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49061.

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Estimation of the number of hidden states is challenging in hidden Markov models. Motivated by the analysis of a specific type of cell adhesion experiments, a new frame-work based on hidden Markov model and double penalized order selection is proposed. The order selection procedure is shown to be consistent in estimating the number of states. A modified Expectation-Maximization algorithm is introduced to efficiently estimate parameters in the model. Simulations show that the proposed framework outperforms existing methods. Applications of the proposed methodology to real data demonstrate the accuracy of estimating receptor-ligand bond lifetimes and waiting times which are essential in kinetic parameter estimation. The second part of the thesis is concerned with prediction of a deterministic response function y at some untried sites given values of y at a chosen set of design sites. The intended application is to computer experiments in which y is the output from a computer simulation and each design site represents a particular configuration of the input variables. A Bayesian version of the cubic spline method commonly used in numerical analysis is proposed, in which the random function that represents prior uncertainty about y is taken to be a specific stationary Gaussian process. An MCMC procedure is given for updating the prior given the observed y values. Simulation examples and a real data application are given to compare the performance of the Bayesian cubic spline with that of two existing methods.
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Staples, Jonathan Peter. "Hidden Markov models for credit risk." Thesis, Imperial College London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440498.

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Webb, Alexandra. "Detecting recombination using hidden markov models." Thesis, University of Oxford, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.510259.

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Ballot, Johan Stephen Simeon. "Face recognition using Hidden Markov Models." Thesis, Stellenbosch : University of Stellenbosch, 2005. http://hdl.handle.net/10019.1/2577.

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This thesis relates to the design, implementation and evaluation of statistical face recognition techniques. In particular, the use of Hidden Markov Models in various forms is investigated as a recognition tool and critically evaluated. Current face recognition techniques are very dependent on issues like background noise, lighting and position of key features (ie. the eyes, lips etc.). Using an approach which specifically uses an embedded Hidden Markov Model along with spectral domain feature extraction techniques, shows that these dependencies may be lessened while high recognition rates are maintained.
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Durey, Adriane Swalm. "Melody spotting using hidden Markov models." Diss., Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04082004-180126/unrestricted/durey%5Fadriane%5Fs%5F200312%5Fphd.pdf.

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Zhang, Chun. "Hidden Markov models for admixture mapping /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2004. http://uclibs.org/PID/11984.

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Tanguay, Donald O. (Donald Ovila). "Hidden Markov models for gesture recognition." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/37796.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
Includes bibliographical references (p. 41-42).
by Donald O. Tanguay, Jr.
M.Eng.
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Kapadia, Sadik. "Discriminative training of hidden Markov models." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.624997.

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Anderson, Michael. "Option pricing using hidden Markov models." Master's thesis, University of Cape Town, 2006. http://hdl.handle.net/11427/10045.

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Includes bibliographical references (leaves 144-149).
This work will present an option pricing model that accommodates parameters that vary over time, whilst still retaining a closed-form expression for option prices: the Hidden Markov Option Pricing Model. This is possible due to the macro-structure of this model and provides the added advantage of ensuring efficient computation of option prices. This model turns out to be a very natural extension to the Black-Scholes model, allowing for time-varying input parameters.
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DESAI, PRANAY A. "SEQUENCE CLASSIFICATION USING HIDDEN MARKOV MODELS." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1116250500.

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Samaria, Ferdinando Silvestro. "Face recognition using Hidden Markov Models." Thesis, University of Cambridge, 1995. https://www.repository.cam.ac.uk/handle/1810/244871.

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This dissertation introduces work on face recognition using a novel technique based on Hidden Markov Models (HMMs). Through the integration of a priori structural knowledge with statistical information, HMMs can be used successfully to encode face features. The results reported are obtained using a database of images of 40 subjects, with 5 training images and 5 test images for each. It is shown how standard one-dimensional HMMs in the shape of top-bottom models can be parameterised, yielding successful recognition rates of up to around 85%. The insights gained from top-bottom models are extended to pseudo two-dimensional HMMs, which offer a better and more flexible model, that describes some of the twodimensional dependencies missed by the standard one-dimensional model. It is shown how pseudo two-dimensional HMMs can be implemented, yielding successful recognition rates of up to around 95%. The performance of the HMMs is compared with the Eigenface approach and various domain and resolution experiments are also carried out. Finally, the performance of the HMM is evaluated in a fully automated system, where database images are cropped automatically.
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32

Foreman, Lindsay Anne. "Bayesian computation for hidden Markov models." Thesis, Imperial College London, 1994. http://hdl.handle.net/10044/1/11490.

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33

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 Financeira
A 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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Marklund, Emil. "Bayesian inference in aggregated hidden Markov models." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-243090.

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Single molecule experiments study the kinetics of molecular biological systems. Many such studies generate data that can be described by aggregated hidden Markov models, whereby there is a need of doing inference on such data and models. In this study, model selection in aggregated Hidden Markov models was performed with a criterion of maximum Bayesian evidence. Variational Bayes inference was seen to underestimate the evidence for aggregated model fits. Estimation of the evidence integral by brute force Monte Carlo integration theoretically always converges to the correct value, but it converges in far from tractable time. Nested sampling is a promising method for solving this problem by doing faster Monte Carlo integration, but it was here seen to have difficulties generating uncorrelated samples.
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35

Dannemann, Jörn. "Inference for hidden Markov models and related models." Göttingen Cuvillier, 2009. http://d-nb.info/1000750442/04.

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36

Chou, Lin-Yi. "Improving the performance of Hierarchical Hidden Markov Models on Information Extraction tasks." The University of Waikato, 2006. http://adt.waikato.ac.nz/public/adt-uow20070212.152608/index.html.

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37

The, Yu-Kai. "Analysis of ion channels with hidden Markov models." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=976048744.

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38

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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39

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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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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Liu, Nianjun. "Hand gesture recognition by Hidden Markov Models /." [St. Lucia, Qld.], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18158.pdf.

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42

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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43

Lancaster, Joseph Paul Jr. "Toward autism recognition using hidden Markov models." Thesis, Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/777.

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44

Keys, Kevin Lawrence. "Hidden Markov Models in Genetics and Linguistics." Thesis, The University of Arizona, 2010. http://hdl.handle.net/10150/146860.

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This document provides an overview of hidden Markov Models (HMMs). It begins with some probability background, including some descriptions of algorithms used in implementing HMMs. Hidden Markov Models come from a class of systems endowed with probabilistic properties that make it useful for modeling situations in which the modeler lacks a full specification of the system in question, but has data generated by the system. This is not altogether different from a car mechanic attempting to understand an automobile motor by studying the emissions from the tailpipe and the response to acceleration, both without ever having peeked under the hood of the vehicle. To construct a theory of hidden Markov models, we first construct a theory of Markov chains; this section assumes an elementary knowledge of probability theory and univariate calculus. The subsequent two sections describe in detail two applications in particular; one in population genetics, and one in computational linguistics. This document is not meant to serve as a comprehensive text on HMMs, or on Markov models: for more technical discussion of HMMs with with many excellent examples, the reader is referred to [2].
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Larson, Jessica. "Hidden Markov Models Predict Epigenetic Chromatin Domains." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10105.

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Epigenetics is an important layer of transcriptional control necessary for cell-type specific gene regulation. We developed computational methods to analyze the combinatorial effect and large-scale organizations of genome-wide distributions of epigenetic marks. Throughout this dissertation, we show that regions containing multiple genes with similar epigenetic patterns are found throughout the genome, suggesting the presence of several chromatin domains. In Chapter 1, we develop a hidden Markov model (HMM) for detecting the types and locations of epigenetic domains from multiple histone modifications. We use this method to analyze a published ChIP-seq dataset of five histone modification marks in mouse embryonic stem cells. We successfully detect domains of consistent epigenetic patterns from ChIP-seq data, providing new insights into the role of epigenetics in longrange gene regulation. In Chapter 2, we expand our model to investigate the genome-wide patterns of histone modifications in multiple human cell lines. We find that chromatin states can be used to accurately classify cell differentiation stage, and that three cancer cell lines can be classified as differentiated cells. We also found that genes whose chromatin states change dynamically in accordance with differentiation stage are not randomly distributed across the genome, but tend to be embedded in multi-gene chromatin domains. Moreover, many specialized gene clusters are associated with stably occupied domains. In the last chapter, we develop a more sophisticated, tiered HMM to include a domain structure in our chromatin annotation. We find that a model with three domains and five sub-states per domain best fits our data. Each state has a unique epigenetic pattern, while still staying true to its domain’s specific functional aspects and expression profiles. The majority of the genome (including most introns and intergenic regions) has low epigenetic signals and is assigned to the same domain. Our model outperforms current chromatin state models due to its increased domain coherency and interpretation.
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46

Sindle, Colin. "Handwritten signature verification using hidden Markov models." Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/53445.

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Thesis (MScEng)--University of Stellenbosch, 2003.
ENGLISH ABSTRACT: Handwritten signatures are provided extensively to verify identity for all types of transactions and documents. However, they are very rarely actually verified. This is because of the high cost of training and employing enough human operators (who are still fallible) to cope with the demand. They are a very well known, yet under-utilised biometric currently performing far below their potential. We present an on-line/dynamic handwritten signature verification system based on Hidden Markov Models, that far out performs human operators in both accuracy and speed. It uses only the local signature features-sampled from an electronic writing tablet-after some novel preprocessing steps, and is a fully automated system in that there are no parameters that need to be manually fine-tuned for different users. Novel verifiers are investigated which attain best equal error rates of between 2% and 5% for different types of high quality deliberate forgeries, and take a fraction of a second to accept or reject an identity claim on a 700 MHz computer.
AFRIKAANSE OPSOMMING: Geskrewe handtekeninge word gereeld gebruik om die identiteit van dokumente en transaksies te bevestig. Aangesien dit duur is in terme van menslike hulpbronne, word die integrit eit daarvan selde nagegaan. Om handtekeninge deur menslike operateurs te verifieër. is ook feilbaar-lOO% akkurate identifikasie is onrealisties. Handtekeninge is uiters akkurate en unieke identifikasie patrone wat in die praktyk nie naastenby tot hul volle potensiaal gebruik word nie. In hierdie navorsing gebruik ons verskuilde Markov modelle om dinamiese handtekeningherkenningstelsels te ontwikkel wat, in terme van spoed en akkuraatheid heelwat meer effektief as operateurs is. Die stelsel maak gebruik van slegs lokale handtekening eienskappe (en verwerkings daarvan) soos wat dit verkry word vanaf 'n elektroniese skryftablet. Die stelsel is ten volle outomaties en geen parameters hoef aangepas te word vir verskillende gebruikers nie. 'n Paar tipes nuwe handtekeningverifieërders word ondersoek en die resulterende gelykbreekpunt vir vals-aanvaardings- en vals-verwerpingsfoute lê tussen 2% en 5% vir verskillende tipes hoë kwaliteit vervalsde handtekeninge. Op 'n tipiese 700 MHz verwerker word die identiteit van 'n persoon ill minder as i sekonde bevestig.
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47

Kordi, Kamran. "Intelligent character recognition using hidden Markov models." Thesis, Loughborough University, 1990. https://dspace.lboro.ac.uk/2134/13786.

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Recognition of printed and hand printed characters has received much attention over the past decade as the need for automated 'document entry' systems assumes a commanding role in office automation. Although, present Optical Character Recognition(OCR) systems have reached a high degree of sophistication as compared to early systems, the design of a robust system which can separate text from images accurately and cope reliably with noisy input and frequent change of font is a formidable task. In this thesis, a novel method of character recognition based on Hidden Markov Modelling (HMM) is initially described. The scheme first describes a training set of characters by their outer contours using Freeman codes; next, the HMM method is applied to capture topological variation of the characters automatically, by looking at typical samples of the different characters. Fonts of similar topology can also be incorporated in one hidden Markov model. Once the model of a character in upright position is derived, the character can be recognized, even, when it has been rotated by multiples of 90 degrees. This technique is further extended to combine structural analysis/description of characters with hidden Markov modelling. In this scheme, a character is first skeletonized and then split to primitives; each primitive is described by hidden Markov models while its Corresponding position with respect to nodes(junctions) where the primitives meet, are recorded. This scheme is virtually font and size independent. A new document classification algorithm based on Fuzzy theory is also proposed which provides an indication of a document's contents in terms of 'text' and 'nontext' portions.
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48

Ryan, Matthew Stephen. "Dynamic character recognition using Hidden Markov Models." Thesis, University of Warwick, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263326.

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Austin, Stephen Christopher. "Hidden Markov models for automatic speech recognition." Thesis, University of Cambridge, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292913.

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Van, der Merwe Hugo Jacobus. "Bird song recognition with Hidden Markov Models /." Thesis, Link to the online version, 2008. http://hdl.handle.net/10019/914.

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