Probabilistic Foundation Models

Content

Probabilistic relational models (PRMs), lifted Inference: lifted variable elimination, lifted branching tree algorithm, model counting (first order and algebraic type), relational probabilistic computational networks

Sequential (e.g. discrete-time) modeling and inference with PRMs, taming of PRMs over time (retrospective and progressive) discrete-time) modeling and inference with PRMs, taming PRMs over time (retrospective and progressive)

Machine learning for PRMs

Decision making and planning with PRMs and under

Causality considerations

Dynamic extensions of the state space: Generative dynamic causal probabilistic-relational models for
stochastic games (genDC-SG-PRMs)

PRMs and LLMs