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multiple importance sampling

November 20, 2015

“Within this unified context, it is possible to interpret that all the MIS algorithms draw samples from a equal-weighted mixture distribution obtained from the set of available proposal pdfs.” In a very special (important?!) week for importance sampling!, Elvira et al. arXived a paper about generalized multiple importance sampling. The setting is the same as […]

optimal mixture weights in multiple importance sampling

December 12, 2014

Multiple importance sampling is back!!! I am always interested in this improvement upon regular importance sampling, even or especially after publishing a recent paper about our AMIS (for adaptive multiple importance sampling) algorithm, so I was quite eager to see what was in Hera He’s and Art Owen’s newly arXived paper. The paper is definitely […]

Adaptive multiple importance sampling resubmited

May 9, 2011

Our AMIS paper (with Jean-Marie Cornuet, Jean-Michel Marin and Antonietta Mira) has been once again revised and resubmitted to Scandinavian Journal of Statistics and arXived as well. Since this is the second round of revisions, the difference is not major with the earlier version… The reason why we needed this revision is that a new […]

importance sampling with multiple MCMC sequences

October 2, 2015

Vivek Roy, Aixian Tan and James Flegal arXived a new paper, Estimating standard errors for importance sampling estimators with multiple Markov chains, where they obtain a central limit theorem and hence standard error estimates when using several MCMC chains to simulate from a mixture distribution as an importance sampling function. Just before I boarded my […]

a new rule for adaptive importance sampling

March 5, 2019

Art Owen and Yi Zhou have arXived a short paper on the combination of importance sampling estimators. Which connects somehow with the talk about multiple estimators I gave at ESM last year in Helsinki. And our earlier AMIS combination. The paper however makes two important assumptions to reach optimal weighting, which is inversely proportional to […]