Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiplicative Factorization of Noisy-Max   The noisy-or and its generalization noisy-max have been utilized to reduce
the complexity of knowledge acquisition. In this paper, we present a new
representation of noisy-max that allows for efficient inference in general
Bayesian networks. Empirical studies show that our method is capable of
computing queries in well-known large medical networks, QMR-DT and CPCS, for
which no previous exact inference method has been shown to perform well.

37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn