Constructing Markov Processes with Random Times of Birth and Death

1987 ◽  
pp. 35-69 ◽  
Author(s):  
R. K. Getoor ◽  
Joseph Glover
Keyword(s):  
Markov Processes ◽  
Random Times

scholarly journals Random Times for Markov Processes with Killing

2021 ◽  
Vol 5 (4) ◽  
pp. 254
Author(s):  
Yuri G. Kondratiev ◽  
José Luís da Silva
Keyword(s):  
Markov Processes ◽  
Schrödinger Operators ◽  
Random Time ◽  
Schrodinger Operators ◽  
Time Behavior ◽  
Non Local ◽  
Time Changes ◽  
Random Times

We consider random time changes in Markov processes with killing potentials. We study how random time changes may be introduced in these Markov processes with killing potential and how these changes may influence their time behavior. As applications, we study the parabolic Anderson problem, the non-local Schrödinger operators as well as the generalized Anderson problem.

scholarly journals Local large deviation principle for Wiener process with random resetting

2019 ◽  
Vol 20 (05) ◽  
pp. 2050032
Author(s):  
A. Logachov ◽  
O. Logachova ◽  
A. Yambartsev
Keyword(s):  
Population Dynamics ◽  
Markov Processes ◽  
Conditional Distribution ◽  
Arrival Time ◽  
Large Deviation Principle ◽  
Large Deviation ◽  
Deviation Principle ◽  
Wiener Processes ◽  
Random Times ◽  
Local Large Deviation Principle

We consider a class of Markov processes with resettings, where at random times, the Markov processes are restarted from a predetermined point or a region. These processes are frequently applied in physics, chemistry, biology, economics, and in population dynamics. In this paper, we establish the local large deviation principle (LLDP) for the Wiener processes with random resettings, where the resettings occur at the arrival time of a Poisson process. Here, at each resetting time, a new resetting point is selected at random, according to a conditional distribution.

Bilinear Markov Processes of Searching for Moving Targets

2001 ◽  
Vol 33 (5-8) ◽  
pp. 13
Author(s):  
Karen G. Dzyubenko ◽  
Arkadiy A. Chikriy
Keyword(s):  
Markov Processes ◽  
Moving Targets

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.

Optimal Control of Markov Processes.

1983 ◽  
Author(s):  
Wendell H. Fleming
Keyword(s):  
Optimal Control ◽  
Markov Processes
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