RxInfer: A Julia package for reactive real-timeBayesian inference

Abstract

Bayesian inference realizes optimal information processing through a full commitment to reasoning by probability theory.The Bayesian framework is positioned at the core of modern AI technology for applications such as speech and image recognition and generation, medical analysis, robot navigation, and more.The framework describes how a rational agent should update its beliefs when new information is revealed by the agent's environment.Unfortunately, perfect Bayesian reasoning is generally intractable, since calculations of (often) very high-dimensional integrals are required for many models of interest.As a result, a number of numerical algorithms for approximating Bayesian inference have been developed and implemented in probabilistic programming packages.Successful methods include the Laplace approximation (Gelman et al., 2015), variants of Monte Carlo (MC) sampling (Salimans et al., n.d.)