Dissertations / Theses on the topic 'Hidden Markov Models'
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Maruotti, Antonello. "Hidden Markov Models for longitudinal data." Doctoral thesis, La Sapienza, 2008. http://hdl.handle.net/11573/917431.
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Dannemann, Jörn. "Inference for hidden Markov models and related models." Göttingen Cuvillier, 2009. http://d-nb.info/1000750442/04.
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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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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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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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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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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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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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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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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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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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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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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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Padilla, Pérez Nicolás. "Heterogeneidad de estados en Hidden Markov models." Tesis, Universidad de Chile, 2014. http://www.repositorio.uchile.cl/handle/2250/129971.
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Magíster en Gestión de OperacionesIngeniero Civil Industrial
Hidden Markov models (HMM) han sido ampliamente usados para modelar comportamientos dinámicos tales como atención del consumidor, navegación en internet, relación con el cliente, elección de productos y prescripción de medicamentos por parte de los médicos. Usualmente, cuando se estima un HMM simultáneamente para todos los clientes, los parámetros del modelo son estimados asumiendo el mismo número de estados ocultos para cada cliente. Esta tesis busca estudiar la validez de este supuesto identificando si existe un potencial sesgo en la estimación cuando existe heterogeneidad en el número de estados. Para estudiar el potencial sesgo se realiza un extenso ejercicio de simulación de Monte Carlo. En particular se estudia: a) si existe o no sesgo en la estimación de parámetros, b) qué factores aumentan o disminuyen el sesgo, y c) qué métodos pueden ser usados para estimar correctamente el modelo cuando existe heterogeneidad en el número de estados. En el ejercicio de simulación, se generan datos utilizando un HMM con dos estados para el 50% de clientes y un HMM con tres estados para el 50% restante. Luego, se utiliza un procedimiento MCMC jerárquico Bayesiano para estimar los parámetros de un HMM con igual número de estados para todos los clientes. En cuanto a la existencia de sesgo, los resultados muestran que los parámetros a nivel individual son recuperados correctamente, sin embargo los parámetros a nivel agregado correspondientes a la distribución de heterogeneidad de los parámetros individuales deben ser reportados cuidadosamente. Esta dificultad es generada por la mezcla de dos segmentos de clientes con distinto comportamiento. En cuanto los factores que afectan el sesgo, los resultados muestran que: 1) cuando la proporción de clientes con dos estados aumenta, el sesgo de los resultados agregados también aumenta; 2) cuando se incorpora heterogeneidad en las probabilidades condicionales, se generan estados duplicados para los clientes con 2 estados y los estados no representan lo mismo para todos los clientes, incrementando el sesgo a nivel agregado; y 3) cuando el intercepto de las probabilidades condicionales es heterogéneo, incorporar variables exógenas puede ayudar a identificar los estados igualmente para todos los clientes. Para reducir los problemas mencionados se proponen dos enfoques. Primero, usar una mezcla de Gaussianas como distribución a priori para capturar heterogeneidad multimodal, y segundo usar un modelo de clase latente con HMMs de distintos número de estados para cada clase. El primer modelo ayuda en representar de mejor forma los resultados agregados. Sin embargo, el modelo no evita que existan estados duplicados para los clientes con menos estados. El segundo modelo captura la heterogeneidad en el número de estados, identificando correctamente el comportamiento a nivel agregado y evitando estados duplicados para clientes con dos estados. Finalmente, esta tesis muestra que en la mayoría de los casos estudiados, el supuesto de un número fijo de estados no genera sesgo a nivel individual cuando se incorpora heterogeneidad. Esto ayuda a mejorar la estimación, sin embargo se deben tomar precauciones al realizar conclusiones usando los resultados agregados.
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Ferrando, Huertas Jaime. "Generating synthetic data through Hidden Markov Models." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235342.
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Machine learning has becoming a trending topic in the last years, being now one of the most demanding careers in computer science. This growing has led to more complex models capable of driving a car or cancer detection, however this models improvements are also thanks to the improvements in computational power. In this study we investigate a data exploration technique for creating synthetic data, a field of Machine learning that does not have as much improvements in the last years. Our project comes from a industrial process where data is a valuable asset, this process has both computational power and power full models but struggles with the availability of the data. In response for this a model for generating data is proposed, aiming to fill the lack of data during data exploration and training of this industrial process. This model consist of a Hidden Markov Model where states represent different distributions the data follows, data is created by traveling through this states with an algorithm that uses the prior distribution of these states in a Dirichlet distribution. The method to infer data distributions from the given data and create this Hidden Markov Model model has been explained along with the technique used to travel between states. Results have been presented showing how the data inferring performed and how the synthetic data reproduces the original one, taking special care for the reproduction of specific features in the original data. To get a better perspective of the data we created we tricked the states for our model, creating data from all of the states or from the states with less prior probability. Results showed that the model is capable of creating data similar to the real one but it struggled with data with a small amount of significant outliers. In conclusion a model to create reliable data has been introduced along with a list of possible improvements.Maskininlärning har blivit ett populärt ämne de senaste åren, nu en av de mest krävande karriärvägarna inom datavetenskap. Att ämnet växt har lett till att mer komplexa modeller utvecklats, kapabla till exempelvis bilkörning och upptäckt av cancer. Dessa framgångar är dock också möjliga på grund av ökad beräkningskraft. I den här undersökningen undersöker vi ett område som utvecklats mindre jämfört med andra de senaste åren, data utforskning. En modell för att generera data föreslås, med målet att åtgärda bristen på data under datautforskning och träning. Denna modell består av ett HMM där tillstånd representerar olika fördelningar av dataflödet. Data skapas genom att färdas genom dessa tillstånd med en algoritm som använder a priorifördelningen av dessa tillstånd i en Dirichlet-fördelning. Metoden för inferens av datadistributionerna från den givna datan och därigenom skapa HMM modellen har förklarats tillsammans med tillvägagångssättet för att förflytta sig mellan tillstånd. Resultat har även presenterats som visar hur inferensen av datan presterade samt hur syntetisk data presterade jämfört med den riktiga. För att få ett bättre perspektiv av datan vi skapat lurade vi tillstånden i vår modell, skapade data från alla tillstånden eller från tillstånden med lägre a priori sannolikhet. Resultaten visade att modellen är kapabel att skapa data lik den riktiga, men den hade svårt med data med en liten andel signifikanta outliers. Sammanfattningsvis så har en modell för att skapa pålitlig data introducerats tillsammans med en lista av möjliga förbättringar.
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Mohammad, Maruf. "Cellular diagnostic systems using hidden Markov models." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/29520.
Full textAbstract:
Radio frequency system optimization and troubleshooting remains one of the most challenging aspects of working in a cellular network. To stay competitive, cellular providers continually monitor the performance of their networks and use this information to determine where to improve or expand services. As a result, operators are saddled with the task of wading through overwhelmingly large amounts of data in order to trouble-shoot system problems. Part of the difficulty of this task is that for many complicated problems such as hand-off failure, clues about the cause of the failure are hidden deep within the statistics of underlying dynamic physical phenomena like fading, shadowing, and interference. In this research we propose that Hidden Markov Models (HMMs) be used as a method to infer signature statistics about the nature and sources of faults in a cellular system by fitting models to various time-series data measured throughout the network. By including HMMs in the network management tool, a provider can explore the statistical relationships between channel dynamics endemic to a cell and its resulting performance.
This research effort also includes a new distance measure between a pair of HMMs that approximates the Kullback-Leibler divergence (KLD). Since there is no closed-form solution to calculate the KLD between the HMMs, the proposed analytical expression is very useful in classification and identification problems. A novel HMM based position location technique has been introduced that may be very useful for applications involving cognitive radios.Ph. D.
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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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Mohammad, Maruf H. "Cellular diagnostic systems using hidden Markov models." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/29520.
Full textAbstract:
Radio frequency system optimization and troubleshooting remains one of the most challenging aspects of working in a cellular network. To stay competitive, cellular providers continually monitor the performance of their networks and use this information to determine where to improve or expand services. As a result, operators are saddled with the task of wading through overwhelmingly large amounts of data in order to trouble-shoot system problems. Part of the difficulty of this task is that for many complicated problems such as hand-off failure, clues about the cause of the failure are hidden deep within the statistics of underlying dynamic physical phenomena like fading, shadowing, and interference. In this research we propose that Hidden Markov Models (HMMs) be used as a method to infer signature statistics about the nature and sources of faults in a cellular system by fitting models to various time-series data measured throughout the network. By including HMMs in the network management tool, a provider can explore the statistical relationships between channel dynamics endemic to a cell and its resulting performance.
This research effort also includes a new distance measure between a pair of HMMs that approximates the Kullback-Leibler divergence (KLD). Since there is no closed-form solution to calculate the KLD between the HMMs, the proposed analytical expression is very useful in classification and identification problems. A novel HMM based position location technique has been introduced that may be very useful for applications involving cognitive radios.Ph. D.
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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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Varenius, Malin. "Using Hidden Markov Models to Beat OMXS30." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-409780.
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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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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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Laurio, Kim. "Finding remote protein homologs with hidden Markov models." Thesis, University of Skövde, Department of Computer Science, 1997. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-293.
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Detecting remote homologs by sequence similarity gets increasingly difficult as the percentage of identical residues decreases. The aim of this work was to investigate if the performance of hidden Markov models could be improved by ignoring the subsequences that exhibit high variability, and only concentrate on the truly conserved regions. This is based on the underlying assumption that these high variability regions could be unnecessary, or even misleading, during search of remote protein homologs.
In this paper we challenge this assumption by identifying the high and low variability regions of multiple alignments and modifying models by focusing them on the conserved regions. The high variability regions are located with information theoretic measures and modeled by free insertion modules, which are special nodes that can be used to model arbitrarily long subsequences with a uniform probability distribution.
The results do not support a definitive conclusion since a few cases exhibit a performance increase, while the general trend is that the performance decreases when ignoring high variability regions. Two supplementary tests suggest that when there is a significant performance loss due to deletion of high variability nodes, a much smaller decrease occurs when the nodes are preserved but the position-specific amino acid distributions are removed. Taken together, these results support the hypothesis that there is some valuable information present in the high variability regions that enable the model to better discriminate between true and false homologs; and that other constructs for the high variability regions could perform better.
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Mattila, Robert. "Hidden Markov models : Identification, control and inverse filtering." Licentiate thesis, KTH, Reglerteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223683.
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The hidden Markov model (HMM) is one of the workhorse tools in, for example, statistical signal processing and machine learning. It has found applications in a vast number of fields, ranging all the way from bioscience to speech recognition to modeling of user interactions in social networks. In an HMM, a latent state transitions according to Markovian dynamics. The state is only observed indirectly via a noisy sensor – that is, it is hidden. This type of model is at the center of this thesis, which in turn touches upon three main themes. Firstly, we consider how the parameters of an HMM can be estimated from data. In particular, we explore how recently proposed methods of moments can be combined with more standard maximum likelihood (ML) estimation procedures. The motivation for this is that, albeit the ML estimate possesses many attractive statistical properties, many ML schemes have to rely on local-search procedures in practice, which are only guaranteed to converge to local stationary points in the likelihood surface – potentially inhibiting them from reaching the ML estimate. By combining the two types of algorithms, the goal is to obtain the benefits of both approaches: the consistency and low computational complexity of the former, and the high statistical efficiency of the latter. The filtering problem – estimating the hidden state of the system from observations – is of fundamental importance in many applications. As a second theme, we consider inverse filtering problems for HMMs. In these problems, the setup is reversed; what information about an HMM-filtering system is exposed by its state estimates? We show that it is possible to reconstruct the specifications of the sensor, as well as the observations that were made, from the filtering system’s posterior distributions of the latent state. This can be seen as a way of reverse engineering such a system, or as using an alternative data source to build a model. Thirdly, we consider Markov decision processes (MDPs) – systems with Markovian dynamics where the parameters can be influenced by the choice of a control input. In particular, we show how it is possible to incorporate prior information regarding monotonic structure of the optimal decision policy so as to accelerate its computation. Subsequently, we consider a real-world application by investigating how these models can be used to model the treatment of abdominal aortic aneurysms (AAAs). Our findings are that the structural properties of the optimal treatment policy are different than those used in clinical practice – in particular, that younger patients could benefit from earlier surgery. This indicates an opportunity for improved care of patients with AAAs.QC 20180301
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Wistrand, Markus. "Hidden Markov models for remote protein homology detection /." Stockholm, 2005. http://diss.kib.ki.se/2006/91-7140-598-4/.
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Flor, Roey. "Template-based sketch recognition using Hidden Markov Models." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/30238.
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Sketch recognition is the process by which the objects in a hand-drawn diagram
can be recognized and identified. We provide a method to recognize
objects in sketches by casting the problem in terms of searching for known
2D template shapes in the sketch. The template is defined as an ordered
polyline and the recognition requires searching for a similarly-shaped sequential
path through the line segments that comprise the sketch. The search
for the best-matching path can be modeled using a Hidden Markov Model
(HMM). We use an efficient dynamic programming method to evaluate the
HMM with further optimizations based on the use of hand-drawn sketches.
The technique we developed can cope with several issues that are common
to sketches such as small gaps and branching. We allow for objects with either
open or closed boundaries by allowing backtracking over the templates.
We demonstrate the algorithm for a variety of templates and scanned drawings.
We show that a likelihood score produced by the results can provide a
meaningful measure of similarity to a template. An example-based method
is presented for setting a meaningful recognition threshold, which can allow
further refinement of results when that template is used again. Limitations
of the algorithm and directions for future work are discussed.
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Erlwein, Christina. "Applications of hidden Markov models in financial modelling." Thesis, Brunel University, 2008. http://bura.brunel.ac.uk/handle/2438/7898.
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Various models driven by a hidden Markov chain in discrete or continuous time are developed to capture the stylised features of market variables whose levels or values constitute as the underliers of financial derivative contracts or investment portfolios. Since the parameters are switching regimes, the changes and developments in the economy as soon as they arise are readily reflected in these models. The change of probability measure technique and the EM algorithm are fundamental techniques utilised in the optimal parameter estimation. Recursive adaptive filters for the state of the Markov chain and other auxiliary processes related to the Markov chain are derived which in turn yield self-tuning dynamic financial models. A hidden Markov model (HMM)-based modelling set-up for commodity prices is developed and the predictability of the gold market under this setting is examined. An Ornstein-Uhlenbeck (OU) model with HMM parameters is proposed and under this set-up, we address two statistical inference issues: the sensitivity of the model to small changes in parameter estimates and the selection of the optimal number of states. The extended OU model is implemented on a data set of 30-day Canadian T-bill yields. An exponential of a Markov-switching OU process plus a compound Poisson process is put forward as a model for the evolution of electricity spot prices. Using a data set compiled by Nord Pool, we illustrate the vast improvements gained in incorporating regimes in the model. A multivariate HMM is employed as a framework in providing the solutions of two asset allocation problems; one involves the mean-variance utility function and the other entails the CVaR constraint. Finally, the valuation of credit default swaps highlights the important considerations necessitated by pricing in a regime-switching environment. Certain numerical schemes are applied to obtain approximations for the default probabilities and swap rates.
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Ljolje, A. "Intonation and phonetic segmentation using hidden Markov models." Thesis, University of Cambridge, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377219.
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Bhan, Nirav. "Inventory estimation from transactions via hidden Markov models." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101470.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.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 (pages 79-81).
Our work solves the problem of inventory tracking in the retail industry using Hidden Markov Models. It has been observed that inventory records are extremely inaccurate in practice (cf. [1{4]). Reasons for this inaccuracy are item losses due to item theft, mishandling, etc. which are unaccounted. Even more important are the lost sales due to lack of items on the shelf, called stockout losses. In several industries, stockout is responsible for billions of dollars of lost sales each year (cf. [4]). In [5], it is estimated that 4% of annual sales are lost due to stockout, across a range of industries. Traditional approaches toward solving the inventory problem have been geared toward designing better inventory management practices, to reduce or account for stock uncertainity. However, such strategies have had limited success in overcoming the effects of inaccurate inventory (cf. [1]). Thus, inventory tracking remains an important unsolved problem. The work done in this thesis is a step toward solving this problem. Our solution follows a novel approach of estimating inventory using accurately available point-of-sales data. A similar approach has been seen in other recent work such as [1, 6, 7]. Our key idea is that when the item is in stockout, no sales are recorded. Thus, by looking at the sequence of sales as a time-series, we can guess the period when stockout has occured. In our work, we nd that under appropriate assumptions, exact stock recovery is possible for all time. To represent the evolution of inventory in a retail store, we use a Hidden Markov Model (HMM), along the lines of [6]. In the latter work, the authors have shown that an HMM-based framework, with Gibbs sampling for estimation, manages to recover stock well in practice. However, their methods are computationally expensive and do not possess any theoretical guarantees. In our work, we introduce a slightly dierent HMM to represent the inventory process, which we call the Sales-Refills model. For this model, we are able to determine inventory level for all times, given enough data. Moreover, our recovery algorithms are easy to implement and computationally fast. We also derive sample complexity bounds which show that our methods are statistically ecient. Our work also solves a related problem viz. accurate demand forecasting in presence of unobservable lost sales (cf. [8{10]). The naive approach of computing a time-averaged sales rate underestimates the demand, as stockout may cause interested customers to leave without purchasing any items (cf. [8, 9]). By modelling the retail process explicitly in terms of sales and refills, our model achieves a natural decoupling of the true demand from other parameters. By explicitly determining instants where stock is empty, we obtain a consistent estimate of the demand. Our work also has consequences for HMM learning. In this thesis, we propose an HMM model which is learnable using simple and highly ecient algorithms. This is not a usual property of HMMs; indeed several problems on HMMs are known to be hard (cf. [11{13]). The learnability of our HMM can be considered a consequence of the following property: We have a few parameters which vary over a finite range, and for each value of the parameters we can identify a signature property of the observation sequence. For the Sales-Refills model, the signature property is the location of longer inter-sale intervals in the observation sequence. This simple idea may lead to practically useful HMMs, as exemplied by our work.
by Nirav Bhan.
S.M.
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Shu, Han. "On-line handwriting recognition using hidden Markov models." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/42603.
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Kim, Hyun Soo M. Eng Massachusetts Institute of Technology. "Two new approaches for learning Hidden Markov Models." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61287.
Full textAbstract:
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 99-100).
Hidden Markov Models (HMMs) are ubiquitously used in applications such as speech recognition and gene prediction that involve inferring latent variables given observations. For the past few decades, the predominant technique used to infer these hidden variables has been the Baum-Welch algorithm. This thesis utilizes insights from two related fields. The first insight is from Angluin's seminal paper on learning regular sets from queries and counterexamples, which produces a simple and intuitive algorithm that efficiently learns deterministic finite automata. The second insight follows from a careful analysis of the representation of HMMs as matrices and realizing that matrices hold deeper meaning than simply entities used to represent the HMMs. This thesis takes Angluin's approach and nonnegative matrix factorization and applies them to learning HMMs. Angluin's approach fails and the reasons are discussed. The matrix factorization approach is successful, allowing us to produce a novel method of learning HMMs. The new method is combined with Baum-Welch into a hybrid algorithm. We evaluate the algorithm by comparing its performance in learning selected HMMs to the Baum-Welch algorithm. We empirically show that our algorithm is able to perform better than the Baum-Welch algorithm for HMMs with at most six states that have dense output and transition matrices. For these HMMs, our algorithm is shown to perform 22.65% better on average by the Kullback-Liebler measure.
by Hyun Soo Kim.
M.Eng.
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Liechty, John Calder. "MCMC methods and continuous-time, hidden Markov models." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.625002.
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