Introduction:
The most basic aspect of modern engineering is the design of operators to act on physical systems in an optimal manner relative to a desired objective – for instance, designing a
control policy to autonomously direct a system or designing a classifier to make decisions regarding
the system. These kinds of problems appear in biomedical science, where physical models are created
with the intention of using them to design tools for diagnosis, prognosis, and therapy.
Methods:
In the classical paradigm, our knowledge regarding the model is certain; however, in
practice, especially with complex systems, our knowledge is uncertain and operators must be designed
while taking this uncertainty into account. The related concepts of intrinsically Bayesian robust
operators and optimal Bayesian operators treat operator design under uncertainty. An objective-based
experimental design procedure is naturally related to operator design: We would like to perform an
experiment that maximally reduces our uncertainty as it pertains to our objective.
Results & Discussion:
This paper provides a nonmathematical review of optimal Bayesian operators
directed at biomedical scientists. It considers two applications important to genomics, structural
intervention in gene regulatory networks and classification.
Conclusion:
The salient point regarding intrinsically Bayesian operators is that uncertainty is
quantified relative to the scientific model, and the prior distribution is on the parameters of this model.
Optimization has direct physical (biological) meaning. This is opposed to the common method of
placing prior distributions on the parameters of the operator, in which case there is a scientific gap between
operator design and the phenomena.
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