Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improved Acyclicity Reasoning for Bayesian Network Structure Learning
  with Constraint Programming   Bayesian networks are probabilistic graphical models with a wide range of
application areas including gene regulatory networks inference, risk analysis
and image processing. Learning the structure of a Bayesian network (BNSL) from
discrete data is known to be an NP-hard task with a superexponential search
space of directed acyclic graphs. In this work, we propose a new polynomial
time algorithm for discovering a subset of all possible cluster cuts, a greedy
algorithm for approximately solving the resulting linear program, and a
generalised arc consistency algorithm for the acyclicity constraint. We embed
these in the constraint programmingbased branch-and-bound solver CPBayes and
show that, despite being suboptimal, they improve performance by orders of
magnitude. The resulting solver also compares favourably with GOBNILP, a
state-of-the-art solver for the BNSL problem which solves an NP-hard problem to
discover each cut and solves the linear program exactly.

19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ