A Linear Approximation Method for Probabilistic Inference

TL;DR

This paper introduces a linear approximation technique for probabilistic inference involving continuous variables, especially useful for complex problems with second order probabilities, by iteratively refining Gaussian-based approximations.

Contribution

It presents a novel linear approximation method for probabilistic inference with continuous variables using Gaussian influence diagrams, enabling iterative refinement.

Findings

Effective approximation of probabilistic inference in continuous variables

Applicable to problems with second order probabilities

Improves computational efficiency of inference processes

Abstract

An approximation method is presented for probabilistic inference with continuous random variables. These problems can arise in many practical problems, in particular where there are "second order" probabilities. The approximation, based on the Gaussian influence diagram, iterates over linear approximations to the inference problem.