Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Expectation Conditional Maximization approach for Gaussian graphical
  models   Bayesian graphical models are a useful tool for understanding dependence
relationships among many variables, particularly in situations with external
prior information. In high-dimensional settings, the space of possible graphs
becomes enormous, rendering even state-of-the-art Bayesian stochastic search
computationally infeasible. We propose a deterministic alternative to estimate
Gaussian and Gaussian copula graphical models using an Expectation Conditional
Maximization (ECM) algorithm, extending the EM approach from Bayesian variable
selection to graphical model estimation. We show that the ECM approach enables
fast posterior exploration under a sequence of mixture priors, and can
incorporate multiple sources of information.

3
The categories of this paper are: physics
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
1
The categories of this paper are: cs
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex