Paper Info
Title
A New Approach for Computing Conditional Probabilities of General Stochastic Processes
Abstract
In this paper Hidden Markov Model algorithms are considered as a method for computing conditional properties of continuous-time stochastic simulation models. The goal is to develop an algorithm that adapts known Hidden Markov Model algorithms for use with proxel-based simulation. It is shown how the Forward- and Viterbi-algorithms can be directly integrated in the proxel-method. The possibility of integrating the more complex Baum-Welch-algorithm is theoretically addressed. Experiments are conducted to determine the practicability of the new approach and to illustrate the type of analysis that is possible.
Year
DOI
Venue
2006
10.1109/ANSS.2006.7
Annual Simulation Symposium
Keywords
Field
DocType
conditional property,general stochastic processes,proxel-based simulation,computing conditional probabilities,hidden markov model algorithm,paper hidden markov model,new approach,complex baum-welch-algorithm,continuous-time stochastic simulation model,speech recognition,hidden markov models,manufacturing,prototypes,baum welch,computational modeling,forward algorithm,stochastic process,hidden markov model,natural languages,probability,baum welch algorithm,stochastic processes,viterbi algorithm,computer science,computer simulation,conditional probability
Forward algorithm,Computer science,Artificial intelligence,Viterbi algorithm,Distributed computing,Stochastic simulation,Mathematical optimization,Markov property,Markov model,Variable-order Markov model,Hidden Markov model,Baum–Welch algorithm,Machine learning
Conference
ISSN
ISBN
Citations 
1080-241X
0-7695-2559-8
0
PageRank 
References 
Authors
0.34
1
5
Name
Order
Citations
PageRank
Fabian Wickborn100.68
Claudia Isensee2111.73
Thomas Simon300.34
Sanja Lazarova-Molnar411818.08
Graham Horton500.34