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Hidden Markov Models for Time Series - Walter Zucchini, Iain L. MacDonald, Roland Langrock

Hidden Markov Models for Time Series

An Introduction Using R, Second Edition
Buch | Hardcover
398 Seiten
2016 | 2nd edition
Chapman & Hall/CRC (Verlag)
978-1-4822-5383-2 (ISBN)
CHF 159,95 inkl. MwSt
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Hidden Markov Models (HMMs) remains a vibrant area of research in statistics, with many new applications appearing since publication of the first edition.
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

Walter Zucchini, Iain K. MacDonald, Roland Langrock

Model structure, properties and methods, Preliminaries: mixtures and Markov chains, Hidden Markov models: definition and properties, Direct maximization of the likelihood, Estimation by the EM algorithm, Forecasting, decoding and state prediction, Model selection and checking, Bayesian inference for Poisson-HMMs, R packages, Extensions, Covariates and other extra dependencies, Continuous-valued state processes, Hidden semi-Markov models as HMMs, HMMs for longitudinal data, 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, Animal behaviour model with feedback, Survival rates of Soay sheep, Examples of R code, The functions, Examples of code using the above functions, Some proofs Factorization needed for forward probabilities, Two results for backward probabilities, Conditional independence of Xt1 and XTt+1, References

Erscheinungsdatum
Reihe/Serie Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Zusatzinfo 65 Tables, black and white; 80 Illustrations, black and white
Sprache englisch
Maße 156 x 234 mm
Gewicht 520 g
Themenwelt Mathematik / Informatik Mathematik
Naturwissenschaften Biologie
ISBN-10 1-4822-5383-6 / 1482253836
ISBN-13 978-1-4822-5383-2 / 9781482253832
Zustand Neuware
Informationen gemäß Produktsicherheitsverordnung (GPSR)
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