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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…
eess.SY2026
Learning Nonlinear Continuous-Time Systems for Formal Uncertainty Propagation and Probabilistic Evaluation
Peter Amorese, Morteza Lahijanian
Nonlinear ordinary differential equations (ODEs) are powerful tools for modeling real-world dynamical systems. However, propagating initial state uncertainty through nonlinear dyna…