Mixture and Hidden Markov Models with R

Mixture and hidden Markov models are statistical models used to model behavioral data, where an observed system occupies a number of distinct "regimes" or unobserved (hidden) states. This book covers both mixture and hidden Markov models, introducing main concepts and issues in the simpler context of mixture models and then leaping to hidden Markov models. It provides practical examples from psychology, financial time series, and climate data, illustrating the use of the authors' depmixS4 package. The book is suitable for advanced students and researchers with an applied background.

Format: Hardback
Length: 267 pages
Publication date: 29 June 2022
Publisher: Springer International Publishing AG

This comprehensive book delves into the realm of mixture and hidden Markov models, offering valuable insights for modeling behavioral data. These statistical models are particularly useful when an observed system encompasses multiple distinct "regimes" or unobserved (hidden) states. Widely employed across various fields such as artificial intelligence, biology, finance, and psychology, these models have proven their effectiveness in capturing the complexities of real-world phenomena. Hidden Markov models, as an extension of mixture models, enable the modeling of transitions between states over time, providing a deeper understanding of the underlying dynamics. By presenting both mixture and hidden Markov models within a single volume, the book facilitates a gradual introduction of key concepts and challenges in a relatively simpler context of mixture models. This approach makes the conceptual leap to hidden Markov models more accessible and seamless.

The book is enriched with numerous practical examples, showcasing the diverse applications of these models. These examples are drawn from the authors' extensive work in psychology, as well as other domains such as financial time series and climate data. Each example is accompanied by detailed explanations and fully reproducible code, utilizing the authors' depmixS4 package, a flexible framework for constructing and estimating mixture and hidden Markov models. Additionally, the accompanying hmmR package provides all the datasets used in the examples, along with additional functionality, making it an invaluable resource for advanced students and researchers with an applied background.

In summary, this book serves as a valuable resource for those seeking to understand and apply mixture and hidden Markov models in their research endeavors. Its comprehensive coverage, practical examples, and user-friendly approach make it an essential tool for advanced students and researchers in the fields of statistics, data analysis, and applied sciences.

Weight: 594g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031014383
Edition number: 1st ed. 2022