9781482253832-1482253836-Hidden Markov Models for Time Series: An Introduction Using R, Second Edition (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

Hidden Markov Models for Time Series: An Introduction Using R, Second Edition (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

ISBN-13: 9781482253832
ISBN-10: 1482253836
Edition: 2
Author: Walter Zucchini, Iain L. MacDonald, Roland Langrock
Publication date: 2016
Publisher: Chapman and Hall/CRC
Format: Hardcover 398 pages
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Book details

ISBN-13: 9781482253832
ISBN-10: 1482253836
Edition: 2
Author: Walter Zucchini, Iain L. MacDonald, Roland Langrock
Publication date: 2016
Publisher: Chapman and Hall/CRC
Format: Hardcover 398 pages

Summary

Hidden Markov Models for Time Series: An Introduction Using R, Second Edition (Chapman & Hall/CRC Monographs on Statistics and Applied Probability) (ISBN-13: 9781482253832 and ISBN-10: 1482253836), written by authors Walter Zucchini, Iain L. MacDonald, Roland Langrock, was published by Chapman and Hall/CRC in 2016. With an overall rating of 3.5 stars, it's a notable title among other Applied (Mathematics) books. You can easily purchase or rent Hidden Markov Models for Time Series: An Introduction Using R, Second Edition (Chapman & Hall/CRC Monographs on Statistics and Applied Probability) (Hardcover) from BooksRun, along with many other new and used Applied books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $18.44.

Description

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

  1. Presents an accessible overview of HMMs
  2. Explores a variety of applications in ecology, finance, epidemiology, climatology, and sociology
  3. Includes numerous theoretical and programming exercises
  4. Provides most of the analysed data sets online

New to the second edition

  1. A total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state process
  2. New case studies on animal movement, rainfall occurrence and capture-recapture data
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