A Comprehensive Performance Evaluation on Iterative Algorithms for Sensitivity Analysis of Continuous-Time Markov Chains

2020 ◽  
Vol E103.A (11) ◽  
pp. 1252-1259
Author(s):  
Yepeng CHENG ◽  
Hiroyuki OKAMURA ◽  
Tadashi DOHI
Keyword(s):  
Sensitivity Analysis ◽  
Performance Evaluation ◽  
Markov Chains ◽  
Continuous Time ◽  
Iterative Algorithms ◽  
Continuous Time Markov Chains ◽  
Comprehensive Performance

scholarly journals Trace Machines for Observing Continuous-Time Markov Chains

2006 ◽  
Vol 153 (2) ◽  
pp. 259-277 ◽  
Author(s):  
Verena Wolf ◽  
Christel Baier ◽  
Mila Majster-Cederbaum
Keyword(s):  
Markov Chains ◽  
Continuous Time ◽  
Continuous Time Markov Chains

Bounds and Approximations for the Transient Behavior of Continuous-Time Markov Chains

1989 ◽  
Vol 3 (2) ◽  
pp. 175-198 ◽  
Author(s):  
Bok Sik Yoon ◽  
J. George Shanthikumar
Keyword(s):  
Markov Chains ◽  
Continuous Time ◽  
First Passage Time ◽  
Transient Behavior ◽  
Passage Time ◽  
Lower And Upper Bounds ◽  
Simple Method ◽  
Continuous Time Markov Chains ◽  
Recent Addition ◽  
Dependent Probability

Discretization is a simple, yet powerful tool in obtaining time-dependent probability distribution of continuous-time Markov chains. One of the most commonly used approaches is uniformization. A recent addition to such approaches is an external uniformization technique. In this paper, we briefly review these different approaches, propose some new approaches, and discuss their performances based on theoretical bounds and empirical computational results. A simple method to get lower and upper bounds for first passage time distribution is also proposed.

scholarly journals Level 2.5 Large Deviations for Continuous-Time Markov Chains with Time Periodic Rates

2018 ◽  
Vol 19 (10) ◽  
pp. 3197-3238 ◽  
Author(s):  
Lorenzo Bertini ◽  
Raphael Chetrite ◽  
Alessandra Faggionato ◽  
Davide Gabrielli
Keyword(s):  
Markov Chains ◽  
Large Deviations ◽  
Continuous Time ◽  
Continuous Time Markov Chains ◽  
Time Periodic
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