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Mixture and hidden markov models with R

Visser, Ingmar.

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Mixture and hidden markov models with R

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mixture and hidden markov models with R/ by Ingmar Visser, Maarten Speekenbrink.
Author:	Visser, Ingmar.
other author:	Speekenbrink, Maarten.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xvi, 267 p. :ill., digital ;24 cm.
[NT 15003449]:	Preface -- Introduction & preliminaries -- 2 Mixture and latent class models -- 3 Mixture and latent class models: Applications -- 4 Hidden Markov model -- 5 Univariate hidden Markov models -- 6 Multivariate hidden Markov models -- 7 Extensions -- References -- Index -- Epilogue.
Contained By:	Springer Nature eBook
Subject:	Markov processes. -
Online resource:	https://doi.org/10.1007/978-3-031-01440-6
ISBN:	9783031014406

Mixture and hidden markov models with R
Visser, Ingmar.

Mixture and hidden markov models with R[electronic resource] /by Ingmar Visser, Maarten Speekenbrink. - Cham :Springer International Publishing :2022. - xvi, 267 p. :ill., digital ;24 cm. - Use R!,2197-5744. - Use R!..

Preface -- Introduction & preliminaries -- 2 Mixture and latent class models -- 3 Mixture and latent class models: Applications -- 4 Hidden Markov model -- 5 Univariate hidden Markov models -- 6 Multivariate hidden Markov models -- 7 Extensions -- References -- Index -- Epilogue.

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.

ISBN: 9783031014406

Standard No.: 10.1007/978-3-031-01440-6doiSubjects--Topical Terms:

532104
Markov processes.

LC Class. No.: QA274.7 / .V57 2022

Dewey Class. No.: 519.233

Mixture and hidden markov models with R
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505 0 $a Preface -- Introduction & preliminaries -- 2 Mixture and latent class models -- 3 Mixture and latent class models: Applications -- 4 Hidden Markov model -- 5 Univariate hidden Markov models -- 6 Multivariate hidden Markov models -- 7 Extensions -- References -- Index -- Epilogue.
520 $a 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.
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