Variational Probabilistic Inference and the QMR-DT Network

Details Variational Probabilistic Inference and the QMR-DT Network Variational Probabilistic Inference and the QMR-DT Network

2011-05-27

arxiv.org/abs/1105.5462v1

Journal Of Artificial Intelligence Research, Volume 10, pages 291-322, 1999

Computer Science

Artificial Intelligence

Scientific paper

10.1613/jair.583

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.

Affiliated with

Also associated with

No associations

Rating

Variational Probabilistic Inference and the QMR-DT Network does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Variational Probabilistic Inference and the QMR-DT Network, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Variational Probabilistic Inference and the QMR-DT Network will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-664478