Encapsulating models and approximate inference programs in probabilistic modules

TL;DR

This paper presents a probabilistic module interface enabling encapsulation of complex models and inference algorithms, facilitating modular, platform-agnostic probabilistic programming with demonstrated implementations.

Contribution

It introduces a novel interface for encapsulating probabilistic models and inference methods, allowing flexible, modular, and platform-independent probabilistic programming.

Findings

Sound approximate inference algorithms for networks of modules

Implementation using learned stochastic inference networks

Demonstration with MCMC and SMC inference programs

Abstract

This paper introduces the probabilistic module interface, which allows encapsulation of complex probabilistic models with latent variables alongside custom stochastic approximate inference machinery, and provides a platform-agnostic abstraction barrier separating the model internals from the host probabilistic inference system. The interface can be seen as a stochastic generalization of a standard simulation and density interface for probabilistic primitives. We show that sound approximate inference algorithms can be constructed for networks of probabilistic modules, and we demonstrate that the interface can be implemented using learned stochastic inference networks and MCMC and SMC approximate inference programs.