AIMar 27, 2013
A Linear Approximation Method for Probabilistic Inference
arXiv:1304.2373v115 citations
Originality Synthesis-oriented
AI Analysis
This addresses inference challenges in practical problems with continuous variables, but appears incremental as it builds on existing Gaussian influence diagram techniques.
The paper tackles probabilistic inference with continuous random variables, particularly for second-order probabilities, by presenting an approximation method based on Gaussian influence diagrams that iterates linear approximations.
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.