Hidden markov models for time series : an introduction using R / Walter Zucchini, Iain L. MacDonald and Roland Langrock.

By:

Zucchini, Walter [author]

Contributor(s):

Material type: Text

TextSeries: Monographs on statistics and applied probability ; 150Publication details: Boca Raton : CRC Press, ©2106.Edition: Second editionDescription: xxviii, 370 pages : illustrations ; 24 cmISBN:

9781482253832

Subject(s):

DDC classification:

519.233 23 Z94

Contents:

Preliminaries: mixtures and Markov chains -- Hidden Markov models: definition and properties -- Estimation by direct maximization of the likelihood -- Estimation by the EM algorithm -- Forecasting, decoding and state prediction -- Model selection and checking -- Bayesian inference for Poisson-hidden Markov models -- R packages -- HMMs with general state-dependent distribution -- Covariates and other extra dependencies -- Continuous-valued state processes -- Hidden semi-Markov models and their representation as HMMs -- HMMs for logitudinal 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 behavior which incorporates feedback -- Estimating the survival rates of Soay sheep from makr-recapture-recovery data -- Examples of R code -- Some proofs.

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.

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Holdings
Item type	Current library	Call number	Status	Date due	Barcode	Item holds
Books	ISI Library, Kolkata	519.233 Z94 (Browse shelf(Opens below))	Available		138376

Total holds: 0

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Previous	519.233 Y65 Markov process with an enumerable infinite number of possible states	519.233 Y65 Operator theoretical treatment of Markoff's process and mean ergodic theorem	519.233 Z19 Invariant probabilities of transition functions /	519.233 Z94 Hidden markov models for time series :	519.234 As836 Branching processes	519.234 As836a Almost sure behaviour of linear functionals of supercritical branching processes	519.234 As836M Martingale central limit theorems and asymptotic estimation theory for multiptype branching processes	Next

Includes bibliographical references and indexes.

Preliminaries: mixtures and Markov chains --
Hidden Markov models: definition and properties --
Estimation by direct maximization of the likelihood --
Estimation by the EM algorithm --
Forecasting, decoding and state prediction --
Model selection and checking --
Bayesian inference for Poisson-hidden Markov models --
R packages --
HMMs with general state-dependent distribution --
Covariates and other extra dependencies --
Continuous-valued state processes --
Hidden semi-Markov models and their representation as HMMs --
HMMs for logitudinal 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 behavior which incorporates feedback --
Estimating the survival rates of Soay sheep from makr-recapture-recovery data --
Examples of R code --
Some proofs.

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.

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