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TALENT: Training in Advanced Low Energy Nuclear Theory
Training the next generation of nuclear physicists
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Course 11: Bayesian Methods and Machine Learning

The TALENT/INT course on "Learning from Data: Bayesian Methods and Machine Learning" will be held at the University of York in York, UK from June 10 to 28, 2019. [Dates are still tentative]

See the preliminary information and contact Dick Furnstahl (furnstahl.1@osu.edu) to get on a mailing list.

Application process:

  • The course is intended for students who have already completed graduate level courses in quantum mechanics. We plan to admit approximately 20-25 students.
  • Applications will open in early 2019.

Course organizers: Christian Forssén (Chalmers University, Sweden), Dick Furnstahl (Ohio State University, USA), Daniel Phillips (Ohio University, USA), Ian Vernon (Durham University, UK)

In recent years there has been an explosion of interest in the use of Bayesian methods in nuclear physics. These methods are being used to quantify the uncertainties in theoretical work on topics ranging from the NN force to high-energy heavy-ion collisions, to develop more reliable extrapolants of nuclear-energy-density functionals towards the dripline, to predict the impact that future NICER observations may have on the equation of state of neutron matter, and to determine whether or not nucleon resonances are present in experimental data. Meanwhile machine learning is gaining increased currency as a method for identifying interesting signals in both experiments and simulations.

While most nuclear-physics Ph.D. students are taught some standard (frequentist) statistics as part of their course work, very few encounter Bayesian methods until they are engaged in research. But Bayesian methods provide a coherent and compelling framework to think about inference, and so can be applied to many important questions in nuclear physics. The overall learning goal of this school is to take students who have had no previous exposure to Bayes’ theorem and show them how it can be applied to problems of parameter estimation, model selection, and machine learning.