2020/10/20 by Michael Backenköhler, Luca Bortolussi, Backenköhler, Michael +5
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #Computational Engineering #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Finance #Markov Chains and Monte Carlo Methods #Quantitative Methods (q-bio.QM) #Statistical Methods and Bayesian Inference #Systems and Control (eess.SY) #and Science (cs.CE) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2010.10096
openalex publication_date 2020/10/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.