Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive Gaussian process approximation for Bayesian inference with
  expensive likelihood functions   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. 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.

25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
1
The categories of this paper are: cs
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph