Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A scalable quasi-Bayesian framework for Gaussian graphical models   This paper deals with the Bayesian estimation of high dimensional Gaussian
graphical models. We develop a quasi-Bayesian implementation of the
neighborhood selection method of Meinshausen and Buhlmann (2006) for the
estimation of Gaussian graphical models. The method produces a product-form
quasi-posterior distribution that can be efficiently explored by parallel
computing. We derive a non-asymptotic bound on the contraction rate of the
quasi-posterior distribution. The result shows that the proposed
quasi-posterior distribution contracts towards the true precision matrix at a
rate given by the worst contraction rate of the linear regressions that are
involved in the neighborhood selection. We develop a Markov Chain Monte Carlo
algorithm for approximate computations, following an approach from Atchade
(2015). We illustrate the methodology with a simulation study. The results show
that the proposed method can fit Gaussian graphical models at a scale unmatched
by other Bayesian methods for graphical models.

29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th