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Mixture and hidden markov models with R

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mixture and hidden markov models with Rby Ingmar Visser, Maarten Speekenbrink.
作者:	Visser, Ingmar.
其他作者:	Speekenbrink, Maarten.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xvi, 267 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Markov processes.
電子資源:	https://doi.org/10.1007/978-3-031-01440-6
ISBN:	9783031014406$q(electronic bk.)

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$q(electronic bk.)

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

181910
Markov processes.

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

Dewey Class. No.: 519.233

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