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cs.LG2026
Learning Markov Processes as Sum-of-Square Forms for Analytical Belief Propagation
Peter Amorese, Morteza Lahijanian
Harnessing the predictive capability of Markov process models requires propagating probability density functions (beliefs) through the model. For many existing models however, beli…
cs.LG2025
Universal Learning of Stochastic Dynamics for Exact Belief Propagation using Bernstein Normalizing Flows
Peter Amorese, Morteza Lahijanian
Predicting the distribution of future states in a stochastic system, known as belief propagation, is fundamental to reasoning under uncertainty. However, nonlinear dynamics often m…