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
New to the second edition
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Walter Zucchini, Iain K. MacDonald, Roland Langrock
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Walter Zucchini, Iain K. MacDonald, Roland LangrockHidden 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 ser. N° de réf. du vendeur 123043554
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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.FeaturesPresents an accessible overview of HMMsExplores a variety of applications in ecology, finance, epidemiology, climatology, and sociologyIncludes numerous theoretical and programming exercisesProvides most of the analysed data sets onlineNew to the second editionA total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state processNew case studies on animal movement, rainfall occurrence and capture-recapture data 398 pp. Englisch. N° de réf. du vendeur 9781482253832
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Buch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.FeaturesPresents an accessible overview of HMMsExplores a variety of applications in ecology, finance, epidemiology, climatology, and sociologyIncludes numerous theoretical and programming exercisesProvides most of the analysed data sets onlineNew to the second editionA total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state processNew case studies on animal movement, rainfall occurrence and capture-recapture data. N° de réf. du vendeur 9781482253832
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