Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An approach to large-scale Quasi-Bayesian inference with spike-and-slab
  priors   We propose a general framework using spike-and-slab prior distributions to
aid with the development of high-dimensional Bayesian inference. Our framework
allows inference with a general quasi-likelihood function. We show that highly
efficient and scalable Markov Chain Monte Carlo (MCMC) algorithms can be easily
constructed to sample from the resulting quasi-posterior distributions.
  We study the large scale behavior of the resulting quasi-posterior
distributions as the dimension of the parameter space grows, and we establish
several convergence results. In large-scale applications where computational
speed is important, variational approximation methods are often used to
approximate posterior distributions. We show that the contraction behaviors of
the quasi-posterior distributions can be exploited to provide theoretical
guarantees for their variational approximations. We illustrate the theory with
some simulation results from Gaussian graphical models, and sparse principal
component analysis.

3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph