1999/05/01 by T. S. Jaakkola, M. I. Jordan · 1 citation
Computer Science · #Approximate inference #Bayesian Modeling and Causal Inference #Fiducial inference #Gaussian Processes and Bayesian Inference #Graphical model #Inference #Machine Learning in Healthcare #Probabilistic logic #Probabilistic relevance model #Set (abstract data type) #Variable elimination #cs.AI
paper · pdf · doi:10.1613/jair.583
published as Journal Of Artificial Intelligence Research, Volume 10, pages 291-322, 1999
openalex publication_date 1999/05/01 · arxiv created 2011/05/27 · arxiv updated 2011/05/30 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
We describe a variational approximation method for efficient inference in large-scale probabilistic models. Variational methods are deterministic procedures that provide approximations to marginal and conditional probabilities of interest. They provide alternatives to approximate inference methods based on stochastic sampling or search. We describe a variational approach to the problem of diagnostic inference in the `Quick Medical Reference' (QMR) network. The QMR network is a large-scale probabilistic graphical model built on statistical and expert knowledge. Exact probabilistic inference is infeasible in this model for all but a small set of cases. We evaluate our variational inference algorithm on a large set of diagnostic test cases, comparing the algorithm to a state-of-the-art stochastic sampling method.