Machine Learning in future intensive care—Classification of stochastic Petri Nets via continuous-time Markov chains

2019 ◽  
pp. 259-273
Author(s):  
Ahmed Hallawa ◽  
Song Zhang ◽  
Arne Peine ◽  
Lukas Martin ◽  
Anke Schmeink ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Markov Chains ◽  
Petri Nets ◽  
Continuous Time ◽  
Stochastic Petri Nets ◽  
Continuous Time Markov Chains

scholarly journals FORMALISMS FOR SPECIFYING MARKOVIAN POPULATION MODELS

2011 ◽  
Vol 22 (04) ◽  
pp. 823-841 ◽  
Author(s):  
THOMAS HENZINGER ◽  
BARBARA JOBSTMANN ◽  
VERENA WOLF
Keyword(s):  
Stochastic Process ◽  
Markov Chains ◽  
Continuous Time ◽  
Population Models ◽  
Ease Of Use ◽  
Stochastic Petri Nets ◽  
Reaction Networks ◽  
Process Algebras ◽  
Continuous Time Markov Chains ◽  
Stochastic Process Algebras

In this survey, we compare several languages for specifying Markovian population models such as queuing networks and chemical reaction networks. All these languages — matrix descriptions, stochastic Petri nets, stoichiometric equations, stochastic process algebras, and guarded command models — describe continuous-time Markov chains, but they differ according to important properties, such as compositionality, expressiveness and succinctness, executability, and ease of use. Moreover, they provide different support for checking the well-formedness of a model and for analyzing a model.

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.

Sign in / Sign up

Export Citation Format

Share Document