Adaptive Importance Sampling for Estimation in Structured Domains

@inproceedings{Ortiz2000AdaptiveIS,
  title={Adaptive Importance Sampling for Estimation in Structured Domains},
  author={Luis E. Ortiz and Leslie Pack Kaelbling},
  booktitle={UAI},
  year={2000}
}
Sampling is an important tool for estimating large, complex sums and integrals over high­ dimensional spaces. For instance, importance sampling has been used as an alternative to exact methods for inference in belief networks. Ideally, we want to have a sampling distribution that pro­ vides optimal-variance estimators. In this paper, we present methods that improve the sampling distribution by systematically adapting it as we obtain information from the samples. We present a stochastic-gradient… CONTINUE READING
Related Discussions
This paper has been referenced on Twitter 3 times. VIEW TWEETS

References

Publications referenced by this paper.
Showing 1-8 of 8 references

Bayes net toolbox for Matlab

Kevin P. Murphy
Available from http: I lwww . cs. berkeley • 1999
View 1 Excerpt

Bayesian inference in econometric mod­ els using Monte Carlo

John Geweke
integration. Econometrica, • 1989

Ortiz andLesliePackKaelbling . Samplingmethodsfor actionselectionin influencediagrams

E. Luis
Murphy . Bayesnet toolbox for Matlab ,

Stochasticsamplingand search in belief updating algorithms for very large Bayesiannetworks

P. Kevin
WorkingNotesof theAAAISpring Symposiumon Search Techniquesfor ProblemSolving Under Uncertaintyand IncompleteInformation

Similar Papers

Loading similar papers…