How to Learn from Experience: Principle of Bayesian Inference (1/4)

by Prof. Roberto Trotta (Imperial College London)

Tuesday 27 Nov 2018, 10:00 → 12:00 Europe/Brussels

E/3rd floor-E.349 - Seminar room (E.349) (Marc de Hemptinne (chemin du Cyclotron, 2, Louvain-la-Neuve))

This is the first lecture of the week given by Prof Roberto Trotta, recipient of the "Chaire Georges Lemaître" for the academic year 2018-2019.

During all week, we will have the chance to follow Roberto's lectures and tutorial dedicated on Bayesian statistics.

 

If you intend to follow the lectures, or simply would like to see only one of them, you MUST register below!

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Abstract:

The problem of inference from noisy and/or partial data is ubiquitous in science. It is
particularly acute in observational disciplines, like astrophysics and cosmology, where we
don't have the luxury of being able to control our experiments.
I will introduce the fundamental principles of Bayesian inference as a complete theory for
how to learn from experience, and contrast this approach with the more traditional
frequentist view of probability. I will also discuss practical Bayesian methods and technology
to determine posterior distributions, including Markov Chain Monte Carlo and related
sampling schemes.

 

 

 

CGM_2018_Lecture_2.pdf