Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Divide and Recombine for Large and Complex Data: Model Likelihood
  Functions using MCMC   In Divide & Recombine (D&R), big data are divided into subsets, each analytic
method is applied to subsets, and the outputs are recombined. This enables deep
analysis and practical computational performance. An innovate D\&R procedure is
proposed to compute likelihood functions of data-model (DM) parameters for big
data. The likelihood-model (LM) is a parametric probability density function of
the DM parameters. The density parameters are estimated by fitting the density
to MCMC draws from each subset DM likelihood function, and then the fitted
densities are recombined. The procedure is illustrated using normal and
skew-normal LMs for the logistic regression DM.

23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg