Mixed Nondeterministic-Probabilistic Automata: Blending graphical probabilistic models with nondeterminism
This work addresses the need for unified modeling frameworks in probabilistic reasoning and programming, offering a foundational approach that blends nondeterminism with probabilistic graphical models.
The paper introduces Mixed Nondeterministic-Probabilistic Automata, a new model that integrates nondeterministic automata with graphical probabilistic models like Bayesian networks, enabling parallel composition, simulation relations, and message passing algorithms.
Graphical models in probability and statistics are a core concept in the area of probabilistic reasoning and probabilistic programming-graphical models include Bayesian networks and factor graphs. In this paper we develop a new model of mixed (nondeterministic/probabilistic) automata that subsumes both nondeterministic automata and graphical probabilistic models. Mixed Automata are equipped with parallel composition, simulation relation, and support message passing algorithms inherited from graphical probabilistic models. Segala's Probabilistic Automatacan be mapped to Mixed Automata.