Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Learning of Noisy-Or Bayesian Networks   This paper considers the problem of learning the parameters in Bayesian
networks of discrete variables with known structure and hidden variables.
Previous approaches in these settings typically use expectation maximization;
when the network has high treewidth, the required expectations might be
approximated using Monte Carlo or variational methods. We show how to avoid
inference altogether during learning by giving a polynomial-time algorithm
based on the method-of-moments, building upon recent work on learning
discrete-valued mixture models. In particular, we show how to learn the
parameters for a family of bipartite noisy-or Bayesian networks. In our
experimental results, we demonstrate an application of our algorithm to
learning QMR-DT, a large Bayesian network used for medical diagnosis. We show
that it is possible to fully learn the parameters of QMR-DT even when only the
findings are observed in the training data (ground truth diseases unknown).

2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int