Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Regression-Based Bayesian Estimation and Structure Learning for
  Nonparanormal Graphical Models   A nonparanormal graphical model is a semiparametric generalization of a
Gaussian graphical model for continuous variables in which it is assumed that
the variables follow a Gaussian graphical model only after some unknown smooth
monotone transformations. We consider a Bayesian approach to inference in a
nonparanormal graphical model in which we put priors on the unknown
transformations through a random series based on B-splines. We use a regression
formulation to construct the likelihood through the Cholesky decomposition on
the underlying precision matrix of the transformed variables and put shrinkage
priors on the regression coefficients. We apply a plug-in variational Bayesian
algorithm for learning the sparse precision matrix and compare the performance
to a posterior Gibbs sampling scheme in a simulation study. We finally apply
the proposed methods to a real data set. KEYWORDS:

5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
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The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
1
The categories of this paper are: cs
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg