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Analysis of Markov Jump Processes under Terminal Constraints
- Publication Year :
- 2020
-
Abstract
- Many probabilistic inference problems such as stochastic filtering or the computation of rare event probabilities require model analysis under initial and terminal constraints. We propose a solution to this bridging problem for the widely used class of population-structured Markov jump processes. The method is based on a state-space lumping scheme that aggregates states in a grid structure. The resulting approximate bridging distribution is used to iteratively refine relevant and truncate irrelevant parts of the state-space. This way the algorithm learns a well-justified finite-state projection yielding guaranteed lower bounds for the system behavior under endpoint constraints. We demonstrate the method's applicability to a wide range of problems such as Bayesian inference and the analysis of rare events.
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2010.10096
- Document Type :
- Working Paper