Bayesian Case Reconstruction

Daniel N. Hennessy1, Bruce G. Buchanan1, and John M. Rosenberg2

1 Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA
{hennessy, buchanan}@cs.pitt.edu
2 Dept of Biological Sciences, University of Pittsburgh, PA
jmr@jmr3.xtal.pitt.edu
http://www.xtal.pitt.edu



Abstract. Bayesian Case Reconstruction (BCR) is a case-based technique that
broadens the coverage of a case library by sampling and recombining pieces of
existing cases to construct a large set of plausible cases. It employs a Bayesian 
Belief Network to evaluate whether implicit dependencies within the original 
cases have been maintained. The belief network is constructed from the
experts limited understanding of the domain theory combined with the data
available in the case library. The cases are the primary reasoning vehicle. The
belief network leverages the available domain model to help evaluate whether
the plausible cases have maintained the necessary internal context. BCR is
applied to the design of screening experiments for Macromolecular Crystallization 
in the Probabilistic Screen Design program. We describe BCR and provide 
an empirical comparison of the Probabilistic Screen Design program
against the current practice in Macromolecular Crystallization.
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