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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
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
The first accessible introduction to the many various wildlife
assessment methods! This book uses a new approach that makes the
full range of methods accessible in a way that has not previously
been possible.
Accompanied by free, user-friendly software to get some "hands-on"
experience with the methods and how they perform in different
contexts.
Das UEbungsbuch stellt eine ausgesuchte Sammlung von
Problemstellungen und Loesungen bereit, die durch eine
Formelsammlung mit den wichtigsten im Buch verwendeten Formeln
abgerundet wird. Zusatzlich wird ein umfangreiches Set von
Programmen in R zur Verfugung gestellt, die zur Aufgabenstellung
und Loesung geschrieben wurden. Der Anhang des Buches beinhaltet
daher auch eine kurze Einfuhrung in die Statistik-Software R. Der
Inhalt, Organisation inklusive Kapitelaufteilung orientiert sich an
dem bei Springer erschienenem Werk "Statistik fur Bachelor- und
Masterstudenten: Eine Einfuhrung fur Wirtschafts- und
Sozialwissenschaftler"
Das Buch fuhrt in die wesentlichen statistischen Konzepte und
Ideen ein und erlautert anhand von Beispielen detailliert deren
Umsetzung. Der Stil ist, anders als bei den meisten
Konkurrenzwerken, betont locker gehalten - ohne dabei auf eine
exakte Darstellung zu verzichten. Das Buch ist speziell auf die
Bedurfnisse von Anfangern im Fach Statistik zugeschnitten und fur
Bachelor- und Masterstudenten aller Disziplinen geeignet - auch zum
Selbststudium."
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