Parameter Estimation in Hidden Markov Models with General State Spaces
Author: T. L. Lai
Publisher:
Published: 2003
Total Pages: 26
ISBN-13:
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Parameter Estimation in Hidden Markov Models
Author: Michael Ryan Roberts
Publisher:
Published: 2001
Total Pages: 110
ISBN-13:
DOWNLOAD EBOOKMixture and Hidden Markov Models with R
Author: Ingmar Visser
Publisher: Springer Nature
Published: 2022-06-28
Total Pages: 277
ISBN-13: 3031014405
DOWNLOAD EBOOKThis 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.
Hidden Markov Models
Author: Przemyslaw Dymarski
Publisher: BoD – Books on Demand
Published: 2011-04-19
Total Pages: 329
ISBN-13: 9533072083
DOWNLOAD EBOOKHidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research.
Parameter Estimation for Hidden Markov Models with Intractable Likelihoods
Author: Thomas A. Dean
Publisher:
Published: 2010
Total Pages: 36
ISBN-13:
DOWNLOAD EBOOKInference in Hidden Markov Models
Author: Olivier Cappé
Publisher: Springer Science & Business Media
Published: 2006-04-12
Total Pages: 656
ISBN-13: 0387289828
DOWNLOAD EBOOKThis 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.
Hidden Markov Models for Time Series
Author: Walter Zucchini
Publisher: CRC Press
Published: 2009-04-28
Total Pages: 298
ISBN-13: 1420010891
DOWNLOAD EBOOKReveals How HMMs Can Be Used as General-Purpose Time Series Models Implements all methods in R Hidden Markov Models for Time Series: An Introduction Using R applies hidden Markov models (HMMs) to a wide range of time series types, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out computations for parameter estimation, model selection and checking, decoding, and forecasting. Illustrates the methodology in action After presenting the simple Poisson HMM, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference. Through examples and applications, the authors describe how to extend and generalize the basic model so it can be applied in a rich variety of situations. They also provide R code for some of the examples, enabling the use of the codes in similar applications. Effectively interpret data using HMMs This book illustrates the wonderful flexibility of HMMs as general-purpose models for time series data. It provides a broad understanding of the models and their uses.
Hidden Markov Models for Time Series
Author: Walter Zucchini
Publisher: CRC Press
Published: 2017-12-19
Total Pages: 370
ISBN-13: 1482253844
DOWNLOAD EBOOKHidden 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
Automatic Feature Learning and Parameter Estimation for Hidden Markov Models Using MCE and Gibbs Sampling
Author: Xuping Zhang
Publisher:
Published: 2009
Total Pages:
ISBN-13:
DOWNLOAD EBOOKSince our model is based on gradient decent methods, the MCE method cannot guarantee a global optimal solution and is very sensitive to initialization. We propose a new learning method based on Gibbs sampling to learn the parameters. The new learning method samples parameters from their individual conditional probability distribution instead to maximize the probability directly. This new method is more robust to initialization, and can generally find a better solution. We also developed a new learning method based on Gibbs sampling to learn parameters for continuous hidden Markov models with multivariate Gaussian mixtures. Because hidden Markov models with multivariate Gaussian mixtures are commonly used HMM models in applications, we propose a learning method based on Gibbs sampling. The proposed method is empirically shown to be more robust than comparable expectation-maximization algorithms. We performed experiments using both synthetic and real data. The results show that both methods work better than the standard HMM methods used in landmine detection applications. Experiments with handwritten digits are also presented. The results show that the HMM-model framework with the automatic learning feature algorithm again performed better than the same framework with the man-made feature.
The Application of Hidden Markov Models in Speech Recognition
Author: Mark Gales
Publisher: Now Publishers Inc
Published: 2008
Total Pages: 125
ISBN-13: 1601981201
DOWNLOAD EBOOKThe Application of Hidden Markov Models in Speech Recognition presents the core architecture of a HMM-based LVCSR system and proceeds to describe the various refinements which are needed to achieve state-of-the-art performance.
