Inference In Hidden Markov Models

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Inference In Hidden Markov Models

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Author : Olivier Cappé
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-08-04

Inference In Hidden Markov Models written by Olivier Cappé and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-08-04 with Business & Economics categories.

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.

Inference For Hidden Markov Models And Related Models

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Author : Jörn Dannemann
language : en
Publisher:
Release Date : 2010

Inference For Hidden Markov Models And Related Models written by Jörn Dannemann and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.

Inference In Hidden Markov Models

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Author : Olivier Cappe
language : en
Publisher:
Release Date : 2005

Inference In Hidden Markov Models written by Olivier Cappe and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Markov processes categories.

Bayesian Inference In Hidden Markov Models Through Reversible Jump Markov Chain Monte Carlo

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Author : Christian P. Robert
language : en
Publisher:
Release Date : 1998

Bayesian Inference In Hidden Markov Models Through Reversible Jump Markov Chain Monte Carlo written by Christian P. Robert and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.

Sequential Inference For Hidden Markov Models

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Author : Michael Leo Ellis
language : en
Publisher:
Release Date : 2018

Sequential Inference For Hidden Markov Models written by Michael Leo Ellis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Markov processes categories.

In many applications data are collected sequentially in time with very short time intervals between observations. If one is interested in using new observations as they arrive in time then non-sequential Bayesian inference methods, such as Markov Chain Monte Carlo (MCMC) sampling, can be too slow. Increasingly, state space models are being used to model nonlinear and non-Gaussian systems. The structure of state space models allows for sequential Bayesian inference so that an approximation to the posterior distribution of interest can be updated as new observations arrive. In special cases, the exact posterior distribution can be updated through conjugate Bayesian inference. However, for the general state space model this is not possible. In quantitative finance hidden Markov models have been used to analyze and forecast percent log returns of an asset or a group of assets. In this thesis the Liu and West [2001] auxiliary particle filter is applied to sequentially update the posterior distribution of a hidden Markov model with unknown state and observation distribution parameters.

Likelihood Based Statistical Inference In Hidden Markov Models

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Author : T. Aittokallio
language : en
Publisher:
Release Date : 1999

Likelihood Based Statistical Inference In Hidden Markov Models written by T. Aittokallio and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with categories.

Hidden Markov Models For Time Series

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Author : Walter Zucchini
language : en
Publisher: CRC Press
Release Date : 2009-04-28

Hidden Markov Models For Time Series written by Walter Zucchini and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-28 with Mathematics categories.

Reveals 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.

Inference And Application Of Likelihood Based Methods For Hidden Markov Models

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Author : Florian Schwaiger
language : en
Publisher:
Release Date : 2013

Inference And Application Of Likelihood Based Methods For Hidden Markov Models written by Florian Schwaiger and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.

Hidden Markov Models And Applications

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Author : Nizar Bouguila
language : en
Publisher: Springer Nature
Release Date : 2022-05-19

Hidden Markov Models And Applications written by Nizar Bouguila and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-19 with Technology & Engineering categories.

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.

Inference In Hidden Markov Processes Sampled At Discrete Times

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Author : Sebastien Roland
language : en
Publisher:
Release Date : 2005

Inference In Hidden Markov Processes Sampled At Discrete Times written by Sebastien Roland and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.

This paper is concerned with the estimation of coefficients of continuous-time hidden Markov models when the observations are sampled at discrete times, with a view towards financial applications. These estimates are commonly computed from discretely and frequently-sampled returns. However, recent findings indicate that these estimators are not robust when the frequency increases due to market microstructure. The present work attempts to reconcile continuous-time modeling and discret-time observations. To this end, we propose a model where all the coefficients of the asset log-price Y are unobservable and follow a Markov process X, which represents the hidden market factors which affect Y. We also suppose that stock prices are observed only discretely at random times T. Under the above setting, the inference problem can be treated as a non-linear filtering problem for X by considering measurements given by the random measure associated to (T(k),Y(k))(k0) From a numerical perspective, we develop and compare optimization methods by means of maximum likelihood and Bayesian paradigm so as to compute the state and parameters estimates. Eventually, we provide empirical evidence of the performance of these approaches on simulated and empirical data sets of index returns.