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Abstract:
Scientists perform experiments to collect evidence supporting one or another hypothesis or
theory.
Experimentation requires decisions about how an experiment will be conducted and analyzed,
such as the necessary sample size, selection of treatments or choice of statistical tests. These
decisions depend on the goals and purpose of the experiment and may be constrained by
available resources and ethical considerations. Prior knowledge is usually available from
previous experiments or existing theories motivating the investigation. The Bayesian
approach provides a coherent framework for combining prior information, theoretical models
and uncertainties regarding unknown quantities to find an experimental design optimizing the
goals of the investigation. Applying methods of Bayesian experimental design may help
increasing the efficiency and the informativeness of an experiment. In this talk I will give an
short introduction to Bayesian inference and decision theory, followed by an overview over
Bayesian experimental design and one example of how to design an Bayesian optimal
experiment.
