Hidden Markov models for time series. An introduction using R (Q5890752)

This introductory book on hidden Markov models for time series is subdivided into three parts, I. Model structure, properties and methods, II. Extensions, III. Applications. The appendices contain examples of R codes and some proofs.NEWLINENEWLINEThe first edition of this book appeared in 2009 [Zbl 1180.62130]. For the present second edition, Roland Langrock was joined as third new author.NEWLINENEWLINEPublisher's description: ``This book 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.NEWLINENEWLINENew 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.''