Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Approximate Bayesian Computation for Finite Mixture Models   Finite mixture models are used in statistics and other disciplines, but
inference for mixture models is challenging due, in part, to the multimodality
of the likelihood function and the so-called label switching problem. We
propose extensions of the Approximate Bayesian Computation-Population Monte
Carlo (ABC-PMC) algorithm as an alternative framework for inference on finite
mixture models. There are several decisions to make when implementing an
ABC-PMC algorithm for finite mixture models, including the selection of the
kernels used for moving the particles through the iterations, how to address
the label switching problem, and the choice of informative summary statistics.
Examples are presented to demonstrate the performance of the proposed ABC-PMC
algorithm for mixture modeling. The performance of the proposed method is
evaluated in a simulation study and for the popular recessional velocity galaxy
data.

31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph