Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive Incremental Mixture Markov chain Monte Carlo   We propose Adaptive Incremental Mixture Markov chain Monte Carlo (AIMM), a
novel approach to sample from challenging probability distributions defined on
a general state-space. While adaptive MCMC methods usually update a parametric
proposal kernel with a global rule, AIMM locally adapts a semiparametric
kernel. AIMM is based on an independent Metropolis-Hastings proposal
distribution which takes the form of a finite mixture of Gaussian
distributions. Central to this approach is the idea that the proposal
distribution adapts to the target by locally adding a mixture component when
the discrepancy between the proposal mixture and the target is deemed to be too
large. As a result, the number of components in the mixture proposal is not
fixed in advance. Theoretically, we prove that there exists a process that can
be made arbitrarily close to AIMM and that converges to the correct target
distribution. We also illustrate that it performs well in practice in a variety
of challenging situations, including high-dimensional and multimodal target
distributions.

8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
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The categories of this paper are: physics
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph