Adaptive Gaussian process surrogates for Bayesian inference

Berkeley Statistics and Machine Learning Discussion Group

ML&Sci Forum

October 29, 2018
1:30pm to 2:30pm
1011 Evans Hall
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This week, Dr. Timur Takhtaganov from LBL will tell us about his recent paper, Adaptive Gaussian process surrogates for Bayesian inference. As he will explain, this work belongs to the class of Bayesian Optimization techniques which can be used to achieve accurate estimates of Bayesian posteriors from a very small number of forward simulations.

The Berkeley Statistics and Machine Learning Discussion Group meets weekly to discuss current applications across a wide variety of research domains and software methodologies. Register here to view, propose and vote for this group's upcoming discussion topics. All interested members of the UC Berkeley and LBL communities are welcome and encouraged to attend. Questions may be directed to François Lanusse.

Speaker(s)

Timur Takhtaganov

Postdoctoral Scholar, Center for Computational Sciences and Engineering
Lawrence Berkeley National Laboratory