2015/09/11 by Avi Pfeffer, Pfeffer, Avi, Brian Ruttenberg +7
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #cs.AI
paper · pdf · doi:10.48550/arxiv.1509.03564
arxiv created 2015/09/11 · arxiv updated 2015/09/14
Probabilistic programming provides the means to represent and reason about complex probabilistic models using programming language constructs. Even simple probabilistic programs can produce models with infinitely many variables. Factored inference algorithms are widely used for probabilistic graphical models, but cannot be applied to these programs because all the variables and factors have to be enumerated. In this paper, we present a new inference framework, lazy factored inference (LFI), that enables factored algorithms to be used for models with infinitely many variables. LFI expands the model to a bounded depth and uses the structure of the program to precisely quantify the effect of the unexpanded part of the model, producing lower and upper bounds to the probability of the query.