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.
翻译:概率和统计的图形模型是概率推理和概率编程模型领域的核心概念,包括贝耶斯网络和要素图。在本文件中,我们开发了一个新的混合(非确定性/概率)自动模型,将非确定性自动模型和图形概率模型相混合。混合自动模型配有平行的构成、模拟关系和支持从图形概率模型中继承的信息传递算法。Segara的概率自动算法将绘制为混合自动模型。