Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Incorporating prior information and borrowing information in
  high-dimensional sparse regression using the horseshoe and variational Bayes   We introduce a sparse high-dimensional regression approach that can
incorporate prior information on the regression parameters and can borrow
information across a set of similar datasets. Prior information may for
instance come from previous studies or genomic databases, and information
borrowed across a set of genes or genomic networks. The approach is based on
prior modelling of the regression parameters using the horseshoe prior, with a
prior on the sparsity index that depends on external information. Multiple
datasets are integrated by applying an empirical Bayes strategy on
hyperparameters. For computational efficiency we approximate the posterior
distribution using a variational Bayes method. The proposed framework is useful
for analysing large-scale data sets with complex dependence structures. We
illustrate this by applications to the reconstruction of gene regulatory
networks and to eQTL mapping.

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The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
1
The categories of this paper are: cs
37
The categories of this paper are: supr-con