Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian test of significance for conditional independence: The
  multinomial model   Conditional independence tests (CI tests) have received special attention
lately in Machine Learning and Computational Intelligence related literature as
an important indicator of the relationship among the variables used by their
models. In the field of Probabilistic Graphical Models (PGM)--which includes
Bayesian Networks (BN) models--CI tests are especially important for the task
of learning the PGM structure from data. In this paper, we propose the Full
Bayesian Significance Test (FBST) for tests of conditional independence for
discrete datasets. FBST is a powerful Bayesian test for precise hypothesis, as
an alternative to frequentist's significance tests (characterized by the
calculation of the \emph{p-value}).

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The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
1
The categories of this paper are: cs
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
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The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga