Mixture and Hidden Markov Models with R von Ingmar Visser | ISBN 9783031014406

Mixture and Hidden Markov Models with R

von Ingmar Visser und Maarten Speekenbrink
Mitwirkende
Autor / AutorinIngmar Visser
Autor / AutorinMaarten Speekenbrink
Buchcover Mixture and Hidden Markov Models with R | Ingmar Visser | EAN 9783031014406 | ISBN 3-031-01440-5 | ISBN 978-3-031-01440-6

“The book is intended to guide the reader step by step on how to apply basic versions of FM and HM models and keep the text generally accessible to a broad audience, mainly from sociology, psychology, and economics, who have not used these models before and would apply them.” (Francesco Bartolucci and Fulvia Pennoni, Psychometrika, Vol. 89 (2), June, 2024)

Mixture and Hidden Markov Models with R

von Ingmar Visser und Maarten Speekenbrink
Mitwirkende
Autor / AutorinIngmar Visser
Autor / AutorinMaarten Speekenbrink

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.