Tesi sul tema "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.
Testo completoBaribault, Carl. "Meta State Generalized Hidden Markov Model for Eukaryotic Gene Structure Identification". ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/1098.
Testo completoFlorez-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.
Testo completoPepper, 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.
Testo completoSantos, 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.
Testo completoA 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.
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.
Testo completoWieworka, 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.
Testo completoBlix, 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.
Testo completoDiss. Stockholm : Handelshögskolan, 2004
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.
Testo completoEjnestrand, Ida, e 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.
Testo completoMcGillivray, 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.
Testo completoDans 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.
Nearing, Grey Stephen. "Diagnostics and Generalizations for Parametric State Estimation". Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/293533.
Testo completoAndersson, 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.
Testo completoSeward, 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.
Testo completoQC 20100811
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.
Testo completoMedeiros, 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.
Testo completoCoordena??o de Aperfei?oamento de Pessoal de N?vel Superior
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
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.
Testo completoAndrés 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
Palancia
Kotsalis, Georgios. "Model reduction for Hidden Markov models". Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38255.
Testo completoIncludes 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.
Schimert, James. "A high order hidden Markov model /". Thesis, Connect to this title online; UW restricted, 1992. http://hdl.handle.net/1773/8939.
Testo completoChong, 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.
Testo completoIncludes bibliographical references (leaves 119-123). Also available in electronic version. Access restricted to campus users.
Kato, Akihiro. "Hidden Markov model-based speech enhancement". Thesis, University of East Anglia, 2017. https://ueaeprints.uea.ac.uk/63950/.
Testo completoStanke, Mario. "Gene prediction with a Hidden Markov model". Doctoral thesis, [S.l.] : [s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=970841310.
Testo completoLott, Paul Christian. "StochHMM| A Flexible Hidden Markov Model Framework". Thesis, University of California, Davis, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3602142.
Testo completoIn 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.
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.
Testo completoSeward, D. C. (DeWitt Clinton). "Graphical analysis of hidden Markov model experiments". Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/36469.
Testo completoIncludes bibliographical references (leaves 60-61).
by DeWitt C. Seward IV.
Ph.D.
Kadhem, Safaa K. "Model fit diagnostics for hidden Markov models". Thesis, University of Plymouth, 2017. http://hdl.handle.net/10026.1/9966.
Testo completoSchwardt, Ludwig. "Efficient Mixed-Order Hidden Markov Model Inference". Thesis, Link to the online version, 2007. http://hdl.handle.net/10019/709.
Testo completoFord, Jason. "Adaptive hidden Markov model estimation and applications". Phd thesis, Australian National University, 1998. http://hdl.handle.net/1885/145631.
Testo completoFord, Jason. "Adaptive hidden Markov model estimation and applications". Thesis, Australian National University, 1998. https://eprints.qut.edu.au/108491/1/jasonford.pdf.
Testo completoBulla, 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.
Testo completoPuigcerver, I. Pérez Joan. "A Probabilistic Formulation of Keyword Spotting". Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/116834.
Testo completo[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
Farges, Eric P. "An analysis-synthesis hidden Markov model of speech". Diss., Georgia Institute of Technology, 1987. http://hdl.handle.net/1853/14775.
Testo completoDey, Arkajit. "Hidden Markov model analysis of subcellular particle trajectories". Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66307.
Testo completoThis 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.
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.
Testo completoENGLISH 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.
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.
Testo completoAlneberg, 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.
Testo completoDawson, Colin Reimer, e Colin Reimer Dawson. "HaMMLeT: An Infinite Hidden Markov Model with Local Transitions". Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/626170.
Testo completoTALARICO, 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.
Testo completoA 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.
Wynne-Jones, Michael. "Model building in neural networks with hidden Markov models". Thesis, University of Edinburgh, 1994. http://hdl.handle.net/1842/284.
Testo completoJiang, Zuliang. "Hidden Markov Model with Binned Duration and Its Application". ScholarWorks@UNO, 2010. http://scholarworks.uno.edu/td/1108.
Testo completoWilhelmsson, Anna, e 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.
Testo completoN ̈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
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.
Testo completoIncludes bibliographical references (leaves 72-76). Also available in electronic version. Access restricted to campus users.
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.
Testo completoLangrock, Roland. "On some special-purpose hidden Markov models". Doctoral thesis, 2011. http://hdl.handle.net/11858/00-1735-0000-0006-B6AF-E.
Testo completoBartnik, Grant. "On Improved Generalization of 5-State Hidden Markov Model-based Internet Traffic Classifiers". Thesis, 2013. http://hdl.handle.net/10214/7237.
Testo completoHarker, William Gordon. "Real time furnace froth state detection using Hidden Markov Models". Thesis, 2013. http://hdl.handle.net/10539/12828.
Testo completoShue, Louis. "On performance analysis of state estimators for hidden Markov models". Phd thesis, 1999. http://hdl.handle.net/1885/147620.
Testo completoLIN, I.-HSIN, e 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.
Testo completo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.
Chen, Chien-Jen, e 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.
Testo completo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.
Bruce-Doust, Riley. "Forgetting properties of finite-state reciprocal processes". Thesis, 2017. http://hdl.handle.net/2440/113380.
Testo completoThesis (M.Phil) -- University of Adelaide, School of Electrical and Electronic Engineering, 2017