Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Model comparison for Gibbs random fields using noisy reversible jump
  Markov chain Monte Carlo   The reversible jump Markov chain Monte Carlo (RJMCMC) method offers an
across-model simulation approach for Bayesian estimation and model comparison,
by exploring the sampling space that consists of several models of possibly
varying dimensions. A naive implementation of RJMCMC to models like Gibbs
random fields suffers from computational difficulties: the posterior
distribution for each model is termed doubly-intractable since computation of
the likelihood function is rarely available. Consequently, it is simply
impossible to simulate a transition of the Markov chain in the presence of
likelihood intractability. A variant of RJMCMC is presented, called noisy
RJMCMC, where the underlying transition kernel is replaced with an
approximation based on unbiased estimators. Based on previous theoretical
developments, convergence guarantees for the noisy RJMCMC algorithm are
provided. The experiments show that the noisy RJMCMC algorithm can be much more
efficient than other exact methods, provided that an estimator with controlled
Monte Carlo variance is used, a fact which is in agreement with the theoretical
analysis.

22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
1
The categories of this paper are: cs
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom