Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
An optimal approximation algorithm for Bayesian inference
Artificial Intelligence
Graphical models for machine learning and digital communication
Graphical models for machine learning and digital communication
An introduction to variational methods for graphical models
Learning in graphical models
Introduction to Monte Carlo methods
Learning in graphical models
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Simulation Approaches to General Probabilistic Inference on Belief Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
On the hardness of approximate reasoning
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Stochastic simulation algorithms for dynamic probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
We present a new method for conducting Monte Carlo inference in graphical models which combines explicit search with generalized importance sampling. The idea is to reduce the variance of importance sampling by searching for significant points in the target distribution. We prove that it is possible to introduce search and still maintain unbiasedness. We then demonstrate our procedure on a few simple inference tasks and show that it can improve the inference quality of standard MCMC methods, including Gibbs sampling, Metropolis sampling, and Hybrid Monte Carlo. This paper extends previous work which showed how greedy importance sampling could be correctly realized in the one-dimensional case.