Inference In Hidden Markov Models
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Author |
: Olivier Cappé |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 656 |
Release |
: 2006-04-12 |
ISBN-10 |
: 9780387289823 |
ISBN-13 |
: 0387289828 |
Rating |
: 4/5 (23 Downloads) |
Synopsis Inference in Hidden Markov Models by : Olivier Cappé
This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states. In a unified way the book covers both models with finite state spaces and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Many examples illustrate the algorithms and theory. This book builds on recent developments to present a self-contained view.
Author |
: Olivier Cappé |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 682 |
Release |
: 2005-08-04 |
ISBN-10 |
: 0387402640 |
ISBN-13 |
: 9780387402642 |
Rating |
: 4/5 (40 Downloads) |
Synopsis Inference in Hidden Markov Models by : Olivier Cappé
This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states. In a unified way the book covers both models with finite state spaces and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Many examples illustrate the algorithms and theory. This book builds on recent developments to present a self-contained view.
Author |
: Jörn Dannemann |
Publisher |
: |
Total Pages |
: 129 |
Release |
: 2010 |
ISBN-10 |
: 3869552476 |
ISBN-13 |
: 9783869552477 |
Rating |
: 4/5 (76 Downloads) |
Synopsis Inference for Hidden Markov Models and Related Models by : Jörn Dannemann
Author |
: Olivier Cappe |
Publisher |
: |
Total Pages |
: 652 |
Release |
: 2005 |
ISBN-10 |
: 1138123420 |
ISBN-13 |
: 9781138123427 |
Rating |
: 4/5 (20 Downloads) |
Synopsis Inference in Hidden Markov Models by : Olivier Cappe
Author |
: Nizar Bouguila |
Publisher |
: Springer Nature |
Total Pages |
: 303 |
Release |
: 2022-05-19 |
ISBN-10 |
: 9783030991425 |
ISBN-13 |
: 3030991423 |
Rating |
: 4/5 (25 Downloads) |
Synopsis Hidden Markov Models and Applications by : Nizar Bouguila
This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a specific task or application, such as estimation or selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. The book also reports advances on classic but difficult problems in HMMs such as inference and feature selection and describes real-world applications of HMMs from several domains. The book pertains to researchers and graduate students, who will gain a clear view of recent developments related to HMMs and their applications.
Author |
: Walter Zucchini |
Publisher |
: CRC Press |
Total Pages |
: 370 |
Release |
: 2017-12-19 |
ISBN-10 |
: 9781482253849 |
ISBN-13 |
: 1482253844 |
Rating |
: 4/5 (49 Downloads) |
Synopsis Hidden Markov Models for Time Series by : Walter Zucchini
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
Author |
: Ingmar Visser |
Publisher |
: Springer Nature |
Total Pages |
: 277 |
Release |
: 2022-06-28 |
ISBN-10 |
: 9783031014406 |
ISBN-13 |
: 3031014405 |
Rating |
: 4/5 (06 Downloads) |
Synopsis Mixture and Hidden Markov Models with R by : Ingmar Visser
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.
Author |
: T. Aittokallio |
Publisher |
: |
Total Pages |
: 27 |
Release |
: 1999 |
ISBN-10 |
: 9521204206 |
ISBN-13 |
: 9789521204203 |
Rating |
: 4/5 (06 Downloads) |
Synopsis Likelihood Based Statistical Inference in Hidden Markov Models by : T. Aittokallio
Author |
: Robert J Elliott |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 374 |
Release |
: 2008-09-27 |
ISBN-10 |
: 9780387848549 |
ISBN-13 |
: 0387848541 |
Rating |
: 4/5 (49 Downloads) |
Synopsis Hidden Markov Models by : Robert J Elliott
As more applications are found, interest in Hidden Markov Models continues to grow. Following comments and feedback from colleagues, students and other working with Hidden Markov Models the corrected 3rd printing of this volume contains clarifications, improvements and some new material, including results on smoothing for linear Gaussian dynamics. In Chapter 2 the derivation of the basic filters related to the Markov chain are each presented explicitly, rather than as special cases of one general filter. Furthermore, equations for smoothed estimates are given. The dynamics for the Kalman filter are derived as special cases of the authors’ general results and new expressions for a Kalman smoother are given. The Chapters on the control of Hidden Markov Chains are expanded and clarified. The revised Chapter 4 includes state estimation for discrete time Markov processes and Chapter 12 has a new section on robust control.
Author |
: David Barber |
Publisher |
: Cambridge University Press |
Total Pages |
: 432 |
Release |
: 2011-08-11 |
ISBN-10 |
: 9780521196765 |
ISBN-13 |
: 0521196760 |
Rating |
: 4/5 (65 Downloads) |
Synopsis Bayesian Time Series Models by : David Barber
The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.