Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Randomized Approximation Algorithm of Logic Sampling   In recent years, researchers in decision analysis and artificial intelligence
(AI) have used Bayesian belief networks to build models of expert opinion.
Using standard methods drawn from the theory of computational complexity,
workers in the field have shown that the problem of exact probabilistic
inference on belief networks almost certainly requires exponential computation
in the worst ease [3]. We have previously described a randomized approximation
scheme, called BN-RAS, for computation on belief networks [ 1, 2, 4]. We gave
precise analytic bounds on the convergence of BN-RAS and showed how to trade
running time for accuracy in the evaluation of posterior marginal
probabilities. We now extend our previous results and demonstrate the
generality of our framework by applying similar mathematical techniques to the
analysis of convergence for logic sampling [7], an alternative simulation
algorithm for probabilistic inference.

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The categories of this paper are: hep-ph
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The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: econ
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
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The categories of this paper are: dg-ga
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The categories of this paper are: q-bio
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The categories of this paper are: eess
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The categories of this paper are: math
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci