Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An approximate Bayesian marginal likelihood approach for estimating
  finite mixtures   Estimation of finite mixture models when the mixing distribution support is
unknown is an important problem. This paper gives a new approach based on a
marginal likelihood for the unknown support. Motivated by a Bayesian Dirichlet
prior model, a computationally efficient stochastic approximation version of
the marginal likelihood is proposed and large-sample theory is presented. By
restricting the support to a finite grid, a simulated annealing method is
employed to maximize the marginal likelihood and estimate the support. Real and
simulated data examples show that this novel stochastic
approximation--simulated annealing procedure compares favorably to existing
methods.

2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int