Backward Simulation in Bayesian Networks

2013/02/27 by Robert Fung, Fung, Robert, Brendan del Favero +1
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #cs.AI

paper · pdf · doi:10.48550/arxiv.1302.6807

Appears in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI1994)

arxiv created 2013/02/27 · arxiv updated 2013/02/28

Abstract

Backward simulation is an approximate inference technique for Bayesian belief networks. It differs from existing simulation methods in that it starts simulation from the known evidence and works backward (i.e., contrary to the direction of the arcs). The technique's focus on the evidence leads to improved convergence in situations where the posterior beliefs are dominated by the evidence rather than by the prior probabilities. Since this class of situations is large, the technique may make practical the application of approximate inference in Bayesian belief networks to many real-world problems.

Related

• Stochastic Simulation of Bayesian Belief Networks
• Simulation Approaches to General Probabilistic Inference on Belief Networks
• A Stratified Simulation Scheme for Inference in Bayesian Belief Networks
• A Randomized Approximation Algorithm of Logic Sampling
• Expectation Propagation for approximate Bayesian inference