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- Semi-Markov Chains and Hidden Semi-Markov Models toward Applications
- Hidden semi-Markov model
- Learning Evolutionary Stages with Hidden Semi-Markov Model for Predicting Social Unrest Events

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Limnios Published Mathematics. The purpose of this article is to present the semi-Markov processes focusing on applications, especially in reliability and dependability.

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

This book is concerned with the estimation of discrete-time semi-Markov and hidden semi-Markov processes. Semi-Markov processes are much more general and better adapted to applications than the Markov ones because sojourn times in any state can be arbitrarily distributed, as opposed to the geometrically distributed sojourn time in the Markov case. Another 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 probabilists and statisticians interested in semi-Markov chains theory, reliability and DNA analysis, and for theoretical oriented reliability and bioinformatics engineers. It can also serve as a text for a six month research-oriented course at a Master or PhD level.

This book is concerned with the estimation of discrete-time semi-Markov and hidden semi-Markov processes. Semi-Markov processes are much more general and better adapted to applications than the Markov ones because sojourn times in any state can be arbitrarily distributed, as opposed to the geometrically distributed sojourn time in the Markov case. Another 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 probabilists and statisticians interested in semi-Markov chains theory, reliability and DNA analysis, and for theoretical oriented reliability and bioinformatics engineers. It can also serve as a text for a six month research-oriented course at a Master or PhD level.

In the last years, several authors studied a class of continuous-time semi-Markov processes obtained by time-changing Markov processes by hitting times of independent subordinators. Such processes are governed by integro-differential convolution equations of generalized fractional type. The aim of this paper is to develop a discrete-time counterpart of such a theory and to show relationships and differences with respect to the continuous time case. We present a class of discrete-time semi-Markov chains which can be constructed as time-changed Markov chains and we obtain the related governing convolution type equations. Such processes converge weakly to those in continuous time under suitable scaling limits.

## Hidden semi-Markov model

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Semi-Markov processes are much more general and better adapted to applications Semi-Markov Chains and Hidden Semi-Markov Models toward Applications DRM-free; Included format: PDF; ebooks can be used on all reading devices.

## Learning Evolutionary Stages with Hidden Semi-Markov Model for Predicting Social Unrest Events

A hidden semi-Markov model HSMM is a statistical model with the same structure as a hidden Markov model except that the unobservable process is semi-Markov rather than Markov. This means that the probability of there being a change in the hidden state depends on the amount of time that has elapsed since entry into the current state. This is in contrast to hidden Markov models where there is a constant probability of changing state given survival in the state up to that time. The model was first published by Leonard E.

#### Duplicate citations

Communications in Statistics-Theory and Methods 33 11 , , Journal of Reliability and Statistical Studies, , Methodology and Computing in Applied Probability 19 4 , , Probability, Statistics and Modelling in Public Health, , DOI 10, , Handbook of Performability Engineering, , Crastes, O.

We review recent advances in the statistical analysis of neuronal spike trains based on Gibbs distributions in a large sense including non stationarity. We evoke some possible applications of Variable Length Markov Chains in this field. Abstract : VLMC allows to model time-series with finite state space and highly-varied dynamics. In this presentation we consider the situation where the realization of the VLMC are not observed directly but through an observation process. The estimation is done by maximization of a penalized likelihood criteria. The strong consistency of the estimator is proved under general assumptions on the model.

Что у нас неверные данные. Джабба нахмурил свой несоразмерно выпуклый лоб. - В чем же тогда проблема. В отчет вкралась какая-то ошибка? - Мидж промолчала. Джабба почувствовал, что она медлит с ответом, и снова нахмурился.

Мидж, тебе отлично известно, что Стратмор всего себя отдает работе.

Social unrest events are common happenings in modern society which need to be proactively handled.

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