Hidden Markov Models for Time Series: An Introduction Using R 2nd Edition PDF ebook

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2nd Edition PDF ebook by author Walter Zucchini; Iain L. MacDonald; Roland Langrock published by Chapman & Hall in 2016 and save up to 80%  compared to the print version of this textbook. With PDF version of this textbook, not only save you money, you can also highlight, add text, underline add post-it notes, bookmarks to pages, instantly search for the major terms or chapter titles, etc.
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eBook Details:

Full title: Hidden Markov Models for Time Series: An Introduction Using R
2nd Edition
Edition: 2nd
Copyright year: 2016
Publisher: Chapman & Hall
Author: Walter Zucchini; Iain L. MacDonald; Roland Langrock
ISBN: 9781482253832, 9781315355207
Format: PDF

Description of Hidden Markov Models for Time Series: An Introduction Using R
2nd Edition:
Hidden Markov Models for Time Series: An Introduction Using R 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

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