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\documentclass{acmabs}

\begin{document}

\Journal{Journal of the ACM}

\refkey{Santos96}

\title{On Linear Potential Functions for Approximating {Bayesian}
Computations}

\author{Eugene Santos, Jr.}

\Pages{399--430}

\Month{May}

\Year{1996}

\Volume{43}

\Number{3}

\maketitle

\begin{abstract}

Probabilistic reasoning suffers from NP-hard implementations. In
particular, the amount of probabilistic information necessary to the
computations is often overwhelming. For example, the size of
conditional probability tables in Bayesian networks has long been a
limiting factor in the general use of these networks. \par We present
a new approach for manipulating the probabilistic information given.
This approach avoids being overwhelmed by essentially compressing the
information using approximation functions called linear potential
functions. We can potentially reduce the information from a
combinatorial amount to roughly linear in the number of random
variable assignments. Furthermore, we can compute these functions
through closed form equations. As it turns out, our approximation
method is quite general and may be applied to other data compression
problems.

\end{abstract}

\begin{categories}

E.4 [\textbf{Coding and Information Theory}];
G.1.2 [\textbf{Numerical Analysis}]: Approximation;
G.1.6 [\textbf{Numerical Analysis}]: Optimization;
I.2.3 [\textbf{Artificial Intelligence}]: Deduction and Theorem
Proving;
I.5 [\textbf{Pattern Recognition}]

\end{categories}

\begin{terms}

Algorithms, Design, Experimentation, Theory

\end{terms}

\begin{keywords}

Artificial intelligence, data compaction and compression, integer programming,
  least squares approximation, pattern recognition, probabilistic reasoning,
  uncertainty

\end{keywords}

\end{document}
