Efficient algorithms for combinatorial problems on graphs with bounded, decomposability—a survey
BIT - Ellis Horwood series in artificial intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fusion and propagation with multiple observations in belief networks
Artificial Intelligence
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Blocking Gibbs sampling in very large probabilistic expert systems
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
Convergence rates for Markov chains
SIAM Review
Approximating MAPs for belief networks is NP-hard and other theorems
Artificial Intelligence
Introduction to Monte Carlo methods
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
A revolution: belief propagation in graphs with cycles
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Nonserial Dynamic Programming
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Optimizing exact genetic linkage computations
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Simulation Approaches to General Probabilistic Inference on Belief Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
A Comparison of Structural CSP Decomposition Methods
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Convergence Properties of the Batch Means Method for Simulation Output Analysis
INFORMS Journal on Computing
Constraint Processing
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Unifying tree decompositions for reasoning in graphical models
Artificial Intelligence
Theory of Relational Databases
Theory of Relational Databases
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Randomized algorithms for the loop cutset problem
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
A general algorithm for approximate inference and its application to hybrid bayes nets
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Empirical evaluation of approximation algorithms for probabilistic decoding
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
HUGS: combining exact inference and Gibbs sampling in junction trees
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
An empirical study of w-cutset sampling for bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
An importance sampling algorithm based on evidence pre-propagation
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Iterative algorithms for state estimation of jump Markov linearsystems
IEEE Transactions on Signal Processing
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
Iterative decoding of compound codes by probability propagation in graphical models
IEEE Journal on Selected Areas in Communications
Notes on Cutset Conditioning on Factor Graphs with Cycles
Proceedings of the 2009 conference on Neural Nets WIRN09: Proceedings of the 19th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, May 28--30 2009
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
SampleSearch: Importance sampling in presence of determinism
Artificial Intelligence
Active tuples-based scheme for bounding posterior beliefs
Journal of Artificial Intelligence Research
Importance sampling-based estimation over AND/OR search spaces for graphical models
Artificial Intelligence
A single-exponential FPT algorithm for the K4- minor cover problem
SWAT'12 Proceedings of the 13th Scandinavian conference on Algorithm Theory
Hi-index | 0.00 |
The paper presents a new sampling methodology for Bayesian networks that samples only a subset of variables and applies exact inference to the rest. Cutset sampling is a network structure-exploiting application of the Rao-Blackwellisation principle to sampling in Bayesian networks. It improves convergence by exploiting memory-based inference algorithms. It can also be viewed as an anytime approximation of the exact cutset-conditioning algorithm developed by Pearl. Cutset sampling can be implemented efficiently when the sampled variables constitute a loop-cutset of the Bayesian network and, more generally, when the induced width of the network's graph conditioned on the observed sampled variables is bounded by a constant w. We demonstrate empirically the benefit of this scheme on a range of benchmarks.