| 书目名称 | Mixture and Hidden Markov Models with R | | 编辑 | Ingmar Visser,Maarten Speekenbrink | | 视频video | | | 概述 | Provides a unified and comprehensive treatment of mixture and hidden Markov models.Provides many practical R examples on applications in the behavioral and social sciences.Suitable for advanced studen | | 丛书名称 | Use R! | | 图书封面 |  | | 描述 | .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. | | 出版日期 | Textbook 2022 | | 关键词 | maximum likelihood estimation; statistical theory; R programming; univariate; multivariate; time series; B | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01440-6 | | isbn_ebook | 978-3-031-01440-6Series ISSN 2197-5736 Series E-ISSN 2197-5744 | | issn_series | 2197-5736 | | copyright | Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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发表于 2025-3-21 18:58:06
