Dynamically Automated Pruning of Universal Probabilistic Programming Languages

Probabilistic programming languages aim to separate the user-specified model from the inference allowing users to focus on model design and leave inference to the compiler and runtime systems. However, the structure of the model affects the inference efficiency. We can utilize specific structures in the model to improve inference efficiency, such as random variables forming trees. In this work, we propose pruning, an approach that automates the forward pass of belief propagation to improve the efficiency of likelihood calculations in statically typed universal probabilistic programming languages (PPLs) by utilizing the tree structures within a model. Specifically, users annotate variables for marginalization, making the likelihood calculation more efficient. We present our method implemented in the probabilistic programming language Miking CorePPL and demonstrate the performance of our approach through a series of case studies in phylogenetics.
Submitted for publicationQC 20240918