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Semi-Markov Chains and Hidden Semi-Markov Models toward Applications

Semi-Markov Chains and Hidden Semi-Markov Models toward Applications
  • Author : Vlad Stefan Barbu,Nikolaos Limnios
  • Publisher : Springer
  • Release : 28 August 2008
GET THIS BOOKSemi-Markov Chains and Hidden Semi-Markov Models toward Applications

Here is a work that adds much to the sum of our knowledge in a key area of science today. It is concerned with the estimation of discrete-time semi-Markov and hidden semi-Markov processes. A unique feature of the book is the use of discrete time, especially useful in some specific applications where the time scale is intrinsically discrete. The models presented in the book are specifically adapted to reliability studies and DNA analysis. The book is mainly intended for applied

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Hidden Semi-Markov Models

Hidden Semi-Markov Models
  • Author : Shun-Zheng Yu
  • Publisher : Morgan Kaufmann
  • Release : 22 October 2015
GET THIS BOOKHidden Semi-Markov Models

Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach to HSMMs, including various HSMMs (such as the

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Semi-Markov Chains and Hidden Semi-Markov Models toward Applications

Semi-Markov Chains and Hidden Semi-Markov Models toward Applications
  • Author : Vlad Stefan Barbu,Nikolaos Limnios
  • Publisher : Springer Science & Business Media
  • Release : 07 January 2009
GET THIS BOOKSemi-Markov Chains and Hidden Semi-Markov Models toward Applications

Here is a work that adds much to the sum of our knowledge in a key area of science today. It is concerned with the estimation of discrete-time semi-Markov and hidden semi-Markov processes. A unique feature of the book is the use of discrete time, especially useful in some specific applications where the time scale is intrinsically discrete. The models presented in the book are specifically adapted to reliability studies and DNA analysis. The book is mainly intended for applied

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Hidden Markov Models for Time Series

Hidden Markov Models for Time Series
  • Author : Walter Zucchini,Iain L. MacDonald,Roland Langrock
  • Publisher : CRC Press
  • Release : 19 December 2017
GET THIS BOOKHidden Markov Models for Time Series

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

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Semi-Markov Models and Applications

Semi-Markov Models and Applications
  • Author : Jacques Janssen,Nikolaos Limnios
  • Publisher : Springer Science & Business Media
  • Release : 01 December 2013
GET THIS BOOKSemi-Markov Models and Applications

This book presents a selection of papers presented to the Second Inter national Symposium on Semi-Markov Models: Theory and Applications held in Compiegne (France) in December 1998. This international meeting had the same aim as the first one held in Brussels in 1984 : to make, fourteen years later, the state of the art in the field of semi-Markov processes and their applications, bring together researchers in this field and also to stimulate fruitful discussions. The set of the subjects of the papers

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Inference in Hidden Markov Models

Inference in Hidden Markov Models
  • Author : Olivier Cappé,Eric Moulines,Tobias Ryden
  • Publisher : Springer Science & Business Media
  • Release : 18 April 2006
GET THIS BOOKInference in Hidden Markov Models

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

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Semi-Markov Models

Semi-Markov Models
  • Author : Jacques Janssen
  • Publisher : Springer Science & Business Media
  • Release : 11 November 2013
GET THIS BOOKSemi-Markov Models

This book is the result of the International Symposium on Semi Markov Processes and their Applications held on June 4-7, 1984 at the Universite Libre de Bruxelles with the help of the FNRS (Fonds National de la Recherche Scientifique, Belgium), the Ministere de l'Education Nationale (Belgium) and the Bernoulli Society for Mathe matical Statistics and Probability. This international meeting was planned to make a state of the art for the area of semi-Markov theory and its applications, to bring together researchers

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Markov Processes for Stochastic Modeling

Markov Processes for Stochastic Modeling
  • Author : Oliver Ibe
  • Publisher : Newnes
  • Release : 22 May 2013
GET THIS BOOKMarkov Processes for Stochastic Modeling

Markov processes are processes that have limited memory. In particular, their dependence on the past is only through the previous state. They are used to model the behavior of many systems including communications systems, transportation networks, image segmentation and analysis, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, population studies, epidemiology, animal and insect migration, queueing systems, resource management, dams, financial engineering, actuarial science, and decision systems. Covering a wide range of areas

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