A New Approach for Computing Conditional Probabilities of General Stochastic Processes

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.

• Type: Conference
• Year: 2006
• Where: ANSS
• Authors: Fabian Wickborn, Claudia Isensee, Thomas Simon, Sanja Lazarova-Molnar, Graham Horton
• Keywords: ANSS 2006, Hidden Markov Model, Markov Model Algorithms, Paper Hidden Markov