Hidden Markov Models for Time Series: An Introduction Using R, Second Edition (Chapman & Hall/CRC Monographs on Statistics and Applied Probability) - Softcover
Synopsis
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
"synopsis" may belong to another edition of this title.
About the Author
Walter Zucchini, Iain K. MacDonald, Roland Langrock
"About this title" may belong to another edition of this title.
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Hidden Markov Models for Time Series: An Introduction Using R, Second Edition (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
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Hidden Markov Models for Time Series (Paperback)
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Taylor & Francis Ltd, London, 2021
ISBN 10: 103217949X
ISBN 13: 9781032179490
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Paperback. Condition: new. Paperback. 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.FeaturesPresents an accessible overview of HMMsExplores a variety of applications in ecology, finance, epidemiology, climatology, and sociologyIncludes numerous theoretical and programming exercisesProvides most of the analysed data sets online New to the second editionA total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state processNew case studies on animal movement, rainfall occurrence and capture-recapture data Hidden Markov Models (HMMs) remains a vibrant area of research in statistics, with many new applications appearing since publication of the first edition. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781032179490
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