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(INT Program June 13  July 8, 2016) Reported by Richard Furnstahl, Dave Higdon, Nicolas Schunck, Andrew Steiner Date posted July 19, 2016 
The INT Program INT162a on "Bayesian Methods in Nuclear Physics"
was held from June 13 through July 8, 2016. It had 51 registered participants and 35 scheduled talks with focused afternoon discussions. Bayesian
statistics is a welldeveloped field, although it has not been part of the traditional education of nuclear theorists. In schematic form, Bayesian statistics
treats the parameters or the model/theory as genuine random variables. It
then uses Bayes theorem of probabilities to provide a recipe to compute
their probability distribution (the "posterior") in terms of prior information
(e.g., about the data) and a likelihood function. For applications to tting
("parameter estimation"), the posterior lets us infer, given the data we have
measured, the most probable values of the parameters and predict values of
observables with confidence intervals. Other applications involve deciding
between alternative explanations or parameterizations ("model selection").
In practice, there are pitfalls in the implementation of this formalism and it
is often a computationally hard problem.
Broad communication was achieved through two morning talks by participants on particular applications of Bayesian methods, supplemented by informal afternoon meetings. In addition, statisticians gave an overview lecture/tutorial on Bayesian inference and computational methods each Monday morning, and Friday were entirely devoted to discussions on specific topics that arose during the week, with impromptu presentations and white board sessions. Among the various topics discussed repeatedly in the program were:
