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Editore: Springer, Berlin|Springer International Publishing|Springer, 2022
ISBN 10: 3031014383 ISBN 13: 9783031014383
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 107,09
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Da: Ria Christie Collections, Uxbridge, Regno Unito
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
EUR 127,06
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Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 158,54
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Aggiungi al carrelloHardcover. Condizione: Brand New. 283 pages. 9.25x6.10x0.79 inches. In Stock.
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Editore: Springer International Publishing AG, Cham, 2022
ISBN 10: 3031014383 ISBN 13: 9783031014383
Lingua: Inglese
Da: Grand Eagle Retail, Mason, OH, U.S.A.
Prima edizione
EUR 137,84
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. This book discusses mixture and hidden Markov models for modeling behavioral data. Mixture and hidden Markov models are statistical models which are useful when an observed system occupies a number of distinct regimes or unobserved (hidden) states. These models are widely used in a variety of fields, including artificial intelligence, biology, finance, and psychology. Hidden Markov models can be viewed as an extension of mixture models, to model transitions between states over time. Covering both mixture and hidden Markov models in a single book allows main concepts and issues to be introduced in the relatively simpler context of mixture models. After a thorough treatment of the theory and practice of mixture modeling, the conceptual leap towards hidden Markov models is relatively straightforward. This book provides many practical examples illustrating the wide variety of uses of the models. These examples are drawn from our own work in psychology, as well as other areas such as financial time series and climate data. Most examples illustrate the use of the authors depmixS4 package, which provides a flexible framework to construct and estimate mixture and hidden Markov models. All examples are fully reproducible and the accompanying hmmR package provides all the datasets used, as well as additional functionality. This book is suitable for advanced students and researchers with an applied background. This book discusses mixture and hidden Markov models for modeling behavioral data. Hidden Markov models can be viewed as an extension of mixture models, to model transitions between states over time. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Editore: Springer International Publishing AG, Cham, 2022
ISBN 10: 3031014383 ISBN 13: 9783031014383
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
Prima edizione
EUR 222,97
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. This book discusses mixture and hidden Markov models for modeling behavioral data. Mixture and hidden Markov models are statistical models which are useful when an observed system occupies a number of distinct regimes or unobserved (hidden) states. These models are widely used in a variety of fields, including artificial intelligence, biology, finance, and psychology. Hidden Markov models can be viewed as an extension of mixture models, to model transitions between states over time. Covering both mixture and hidden Markov models in a single book allows main concepts and issues to be introduced in the relatively simpler context of mixture models. After a thorough treatment of the theory and practice of mixture modeling, the conceptual leap towards hidden Markov models is relatively straightforward. This book provides many practical examples illustrating the wide variety of uses of the models. These examples are drawn from our own work in psychology, as well as other areas such as financial time series and climate data. Most examples illustrate the use of the authors depmixS4 package, which provides a flexible framework to construct and estimate mixture and hidden Markov models. All examples are fully reproducible and the accompanying hmmR package provides all the datasets used, as well as additional functionality. This book is suitable for advanced students and researchers with an applied background. This book discusses mixture and hidden Markov models for modeling behavioral data. Hidden Markov models can be viewed as an extension of mixture models, to model transitions between states over time. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Da: Majestic Books, Hounslow, Regno Unito
EUR 103,00
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Aggiungi al carrelloCondizione: New. pp. 267 This item is printed on demand.