jti and sparta: Time and Space Efficient Packages for Model-Based Prediction in Large Bayesian Networks

jti and sparta: Time and Space Efficient Packages for Model-Based Prediction in Large Bayesian Networks

Bayesian networks, junction trees, sparse tables, R, C++.

A Bayesian network is a multivariate (potentially very high dimensional) probabilistic
model formed by combining lower-dimensional components. In Bayesian networks, the
computation of conditional probabilities is fundamental for model-based predictions. This
is usually done based on message passing algorithms that utilize conditional independence
structures. In this paper, we deal with a specific message passing algorithm that exploits a
second structure called a junction tree and hence is known as the junction tree algorithm
(JTA). In Bayesian networks for discrete variables with finite state spaces, there is a
fundamental problem in high dimensions: A discrete distribution is represented by a table
of values, and in high dimensions, such tables can become prohibitively large. In JTA, such
tables must be multiplied which can lead to even larger tables. The jti package meets this
challenge by using the package sparta by implementing methods that efficiently handle
multiplication and marginalization of sparse tables through JTA. The two packages are
written in the R programming language and are freely available from the Comprehensive
R Archive Network.

Mads Lindskou

https://www.jstatsoft.org/article/view/v111i02

Aalborg University

November 2024

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