[PDF] Mixture And Hidden Markov Models With R eBook

Author : Ingmar Visser
Publisher : Springer Nature
Page : 277 pages
File Size : 28,84 MB
Release : 2022-06-28
Category : Mathematics
ISBN : 3031014405

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This book discusses mixture and hidden Markov models for modeling behavioral data. Mixture and hidden Markov models are statistical models which are useful when an observed system occupies a number of distinct “regimes” or unobserved (hidden) states. These models are widely used in a variety of fields, including artificial intelligence, biology, finance, and psychology. Hidden Markov models can be viewed as an extension of mixture models, to model transitions between states over time. Covering both mixture and hidden Markov models in a single book allows main concepts and issues to be introduced in the relatively simpler context of mixture models. After a thorough treatment of the theory and practice of mixture modeling, the conceptual leap towards hidden Markov models is relatively straightforward. This book provides many practical examples illustrating the wide variety of uses of the models. These examples are drawn from our own work in psychology, as well as other areas such as financial time series and climate data. Most examples illustrate the use of the authors’ depmixS4 package, which provides a flexible framework to construct and estimate mixture and hidden Markov models. All examples are fully reproducible and the accompanying hmmR package provides all the datasets used, as well as additional functionality. This book is suitable for advanced students and researchers with an applied background.

Author : Walter Zucchini
Publisher : CRC Press
Page : 370 pages
File Size : 22,65 MB
Release : 2017-12-19
Category : Mathematics
ISBN : 1482253844

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Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models (HMMs) as general-purpose models for time series data. The book provides a broad understanding of the models and their uses. After presenting the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for HMMs. Through examples and applications, the authors describe how to extend and generalize the basic model so that it can be applied in a rich variety of situations. The book demonstrates how HMMs can be applied to a wide range of types of time series: continuous-valued, circular, multivariate, binary, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out the computations. Features Presents an accessible overview of HMMs Explores a variety of applications in ecology, finance, epidemiology, climatology, and sociology Includes numerous theoretical and programming exercises Provides most of the analysed data sets online New to the second edition A total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state process New case studies on animal movement, rainfall occurrence and capture-recapture data

Author : Walter Zucchini
Publisher : CRC Press
Page : 298 pages
File Size : 11,8 MB
Release : 2009-04-28
Category : Mathematics
ISBN : 1420010891

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Reveals How HMMs Can Be Used as General-Purpose Time Series Models Implements all methods in R Hidden Markov Models for Time Series: An Introduction Using R applies hidden Markov models (HMMs) to a wide range of time series types, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out computations for parameter estimation, model selection and checking, decoding, and forecasting. Illustrates the methodology in action After presenting the simple Poisson HMM, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference. Through examples and applications, the authors describe how to extend and generalize the basic model so it can be applied in a rich variety of situations. They also provide R code for some of the examples, enabling the use of the codes in similar applications. Effectively interpret data using HMMs This book illustrates the wonderful flexibility of HMMs as general-purpose models for time series data. It provides a broad understanding of the models and their uses.

Author : Jörn Dannemann
Publisher : Cuvillier Verlag
Page : 140 pages
File Size : 21,86 MB
Release : 2010-03-04
Category : Business & Economics
ISBN : 3736932472

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Hidden Markov models (HMMs) and other latent variable models form complex, flexible frameworks for univariate and multivariate data structures. In the last two decades models with latent variables have entered almost all fields of statistical applications. It is common for these models that unobserved variables are introduced to model a complex data structure given by the observables. A major advantage of latent structures is the principle simplicity and the accessibility to practitioners as well as their application-driven interpretations rather than black box systems. In this dissertation the statistical methodology of HMMs and related models is extended in certain aspects and illustrated by several applications from various fields, including epileptic seizures, financial time series and a dental health trail. We first investigate testing problems for HMMs under nonstandard conditions, namely when the true parameter lies on the boundary. In practical applications of HMMs, non-standard testing problems are frequently encountered, e.g. testing for the probability of staying in a certain unobserved state being zero. We derive the relevant asymptotic distribution theory for the likelihood ratio test in HMMs under these conditions. A number of examples with particular relevance in the HMM framework are examined. Secondly, we are concerned with testing for the number of states in HMMs and switching regression models, in particular, testing for two states in an HMM, and testing for two components in switching regression models. The specification of the number of states is very important in all models with discrete latent variables, and performing statistical testing of such hypotheses is one way to deal with this problem. For testing for homogeneity or for two components in finite mixtures the modified likelihood ratio test is a well-developed method. Based on this approach we propose a test for two states in HMMs. Testing for two states is of primary interest in particular for HMMs, since a two-state HMM represents the smallest non-trivial member of this model class. We derive the asymptotic distribution for the modified likelihood ratio test with independence assumption under the hypothesis of a two-state HMM. In addition, we propose a test for two components in switching regression models with independent or Markov-dependent regime. In the third part we depart from the classical parametric framework and relax the parametric assumptions, aiming for more flexible models, which reduce systematic errors caused by model misspecification and give rise to model validation techniques. We propose a parametric as well as a semiparametric approach to this problem. In particular, the latter one introduces a new flavor to hidden Markov modeling by linking recently developed semiparametric mixture models to the HMM framework. We discuss identifiability and propose an estimation procedure to semiparametric two-state HMMs based on the expectation-maximization algorithm. This enables extensions of modern estimation techniques in semiparametric mixtures like log-concave density estimation to HMMs.

Author : Nizar Bouguila
Publisher : Springer Nature
Page : 303 pages
File Size : 30,53 MB
Release : 2022-05-19
Category : Technology & Engineering
ISBN : 3030991423

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This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a specific task or application, such as estimation or selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. The book also reports advances on classic but difficult problems in HMMs such as inference and feature selection and describes real-world applications of HMMs from several domains. The book pertains to researchers and graduate students, who will gain a clear view of recent developments related to HMMs and their applications.

Author : Mariette Awad
Publisher : Apress
Page : 263 pages
File Size : 50,16 MB
Release : 2015-04-27
Category : Computers
ISBN : 1430259906

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Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.

Author : Przemyslaw Dymarski
Publisher : BoD – Books on Demand
Page : 329 pages
File Size : 17,7 MB
Release : 2011-04-19
Category : Computers
ISBN : 9533072083

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Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research.

Author : Weixin Yao
Publisher : CRC Press
Page : 398 pages
File Size : 36,81 MB
Release : 2024-04-18
Category : Mathematics
ISBN : 1040009875

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Mixture models are a powerful tool for analyzing complex and heterogeneous datasets across many scientific fields, from finance to genomics. Mixture Models: Parametric, Semiparametric, and New Directions provides an up-to-date introduction to these models, their recent developments, and their implementation using R. It fills a gap in the literature by covering not only the basics of finite mixture models, but also recent developments such as semiparametric extensions, robust modeling, label switching, and high-dimensional modeling. Features Comprehensive overview of the methods and applications of mixture models Key topics include hypothesis testing, model selection, estimation methods, and Bayesian approaches Recent developments, such as semiparametric extensions, robust modeling, label switching, and high-dimensional modeling Examples and case studies from such fields as astronomy, biology, genomics, economics, finance, medicine, engineering, and sociology Integrated R code for many of the models, with code and data available in the R Package MixSemiRob Mixture Models: Parametric, Semiparametric, and New Directions is a valuable resource for researchers and postgraduate students from statistics, biostatistics, and other fields. It could be used as a textbook for a course on model-based clustering methods, and as a supplementary text for courses on data mining, semiparametric modeling, and high-dimensional data analysis.

Author : Mark Gales
Publisher : Now Publishers Inc
Page : 125 pages
File Size : 24,36 MB
Release : 2008
Category : Automatic speech recognition
ISBN : 1601981201

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The Application of Hidden Markov Models in Speech Recognition presents the core architecture of a HMM-based LVCSR system and proceeds to describe the various refinements which are needed to achieve state-of-the-art performance.

Author : Gregory R. Hancock
Publisher : IAP
Page : 382 pages
File Size : 13,39 MB
Release : 2007-11-01
Category : Mathematics
ISBN : 1607526344

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The current volume, Advances in Latent Variable Mixture Models, contains chapters by all of the speakers who participated in the 2006 CILVR conference, providing not just a snapshot of the event, but more importantly chronicling the state of the art in latent variable mixture model research. The volume starts with an overview chapter by the CILVR conference keynote speaker, Bengt Muthén, offering a “lay of the land” for latent variable mixture models before the volume moves to more specific constellations of topics. Part I, Multilevel and Longitudinal Systems, deals with mixtures for data that are hierarchical in nature either due to the data’s sampling structure or to the repetition of measures (of varied types) over time. Part II, Models for Assessment and Diagnosis, addresses scenarios for making judgments about individuals’ state of knowledge or development, and about the instruments used for making such judgments. Finally, Part III, Challenges in Model Evaluation, focuses on some of the methodological issues associated with the selection of models most accurately representing the processes and populations under investigation. It should be stated that this volume is not intended to be a first exposure to latent variable methods. Readers lacking such foundational knowledge are encouraged to consult primary and/or secondary didactic resources in order to get the most from the chapters in this volume. Once armed with the basic understanding of latent variable methods, we believe readers will find this volume incredibly exciting.