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E-book
Author Zucchini, W., author.

Title Hidden Markov models for time series : an introduction using R / Walter Zucchini, Iain L. MacDonald, Roland Langrock
Edition Second edition
Published Boca Raton, FL : CRC Press, [2016]
©2016

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Description 1 online resource (xxviii, 370 pages) : illustrations
Series Monographs on statistics and applied probability ; 150
Monographs on statistics and applied probability (Series) ; 150.
Contents Preliminaries: mixtures and Markov chains -- Hidden Markov models: definitions and properties -- Estimation by direct maximization of the likelihood -- Estimation by the EM algorithm -- Model selection and checking -- Bayesian inference for Poisson-hidden Markov models -- R packages -- HMMs with general state-dependent distrubution -- Covariates and other extra dependencies -- Continuous-valued state processes -- Hidden semi-Markov models and their representation as HMMs -- HMMs for longitudinal data -- Introduction to applications -- Epileptic seizures -- Daily rainfall occurrence -- Eruptions of the Old Faithful geyser -- HMMs for animal movement -- Wind direction at Koeberg -- Models for financial series -- Births at Edendale Hospital -- Homicides and suicides in Cape Town, 1986-1991 -- A model for animal behaviour which incorporates feedback -- Estimating the survival rates of Soay sheep from mark-recapture-recovery data
Summary 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. -- Provided by publisher
Bibliography Includes bibliographical references and index
Notes Print version record
Subject Time-series analysis.
Markov processes.
R (Computer program language)
MATHEMATICS -- Applied.
MATHEMATICS -- Probability & Statistics -- General.
Markov processes
R (Computer program language)
Time-series analysis
Form Electronic book
Author MacDonald, Iain L., author.
Langrock, Roland, 1983- author.
ISBN 9781482253849
1482253844
9781315372488
1315372487