Markov processes with identical last exit distributions

1982 ◽  
Vol 59 (1) ◽  
pp. 67-75 ◽  
Author(s):  
Joseph Glover
Keyword(s):  
Markov Processes ◽  
Exit Distributions

On entrance—exit distributions of Markov processes

1978 ◽  
Vol 15 (01) ◽  
pp. 78-86
Author(s):  
Cristina Gzyl ◽  
Henryk Gzyl
Keyword(s):  
Markov Processes ◽  
Additive Functional ◽  
Stochastic Integrals ◽  
Standard Process ◽  
Integration By Parts ◽  
Infinitesimal Generators ◽  
The Right ◽  
Continuous Inverse ◽  
Exit Distributions

We use a result on integration by parts for stochastic integrals together with a technique developed by Getoor in [6], to express entrance—exit distributions for a standard process X, and a set Φ which is the support of a continuous additive functional C, in terms of the infinitesimal generators of semigroups associated with the time-changed process (X τ t ), where (τ t ) is the right-continuous inverse of C.

On entrance—exit distributions of Markov processes

1978 ◽  
Vol 15 (1) ◽  
pp. 78-86
Author(s):  
Cristina Gzyl ◽  
Henryk Gzyl
Keyword(s):  
Markov Processes ◽  
Additive Functional ◽  
Stochastic Integrals ◽  
Standard Process ◽  
Integration By Parts ◽  
Infinitesimal Generators ◽  
The Right ◽  
Continuous Inverse ◽  
Exit Distributions

We use a result on integration by parts for stochastic integrals together with a technique developed by Getoor in [6], to express entrance—exit distributions for a standard process X, and a set Φ which is the support of a continuous additive functional C, in terms of the infinitesimal generators of semigroups associated with the time-changed process (Xτt), where (τt) is the right-continuous inverse of C.

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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