288 pp. per issue
6 x 9, illustrated
2014 Impact factor:

Neural Computation

November 2018, Vol. 30, No. 11, Pages 3072-3094
(doi: 10.1162/neco_a_01127)
© 2018 Massachusetts Institute of Technology
Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions
Article PDF (1.11 MB)
We consider Bayesian inference problems with computationally intensive likelihood functions. We propose a Gaussian process (GP)–based method to approximate the joint distribution of the unknown parameters and the data, built on recent work (Kandasamy, Schneider, & Póczos, 2015). In particular, we write the joint density approximately as a product of an approximate posterior density and an exponentiated GP surrogate. We then provide an adaptive algorithm to construct such an approximation, where an active learning method is used to choose the design points. With numerical examples, we illustrate that the proposed method has competitive performance against existing approaches for Bayesian computation.