Lazy Factored Inference for Functional Probabilistic Programming
This enables more efficient inference for complex probabilistic models in AI and machine learning, though it is an incremental improvement over existing factored methods.
The paper tackles the problem of applying factored inference algorithms to probabilistic programs with infinitely many variables by introducing lazy factored inference (LFI), which expands the model to a bounded depth and uses program structure to quantify unexpanded parts, producing probability bounds for queries.
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.