Special Lectures

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

by Prof. Roberto Trotta (Imperial College London)

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

E/3rd floor-E.349 - Seminar room (E.349)

Marc de Hemptinne (chemin du Cyclotron, 2, Louvain-la-Neuve)

Bâtiment de Hemptinne, Chemin du Cyclotron, 2 1348 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!


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.




Organized by

Christophe Ringeval

  • Alina Kleimenova
  • Ambresh Shivaji
  • Caroline Vandenplas
  • Chiara Arina
  • Christophe Delaere
  • Christophe Ringeval
  • Céline Degrande
  • Florian Bury
  • Giacomo Bruno
  • Hesham El Faham
  • Jan Govaerts
  • Jan Hajer
  • Jerome de Favereau
  • Jinu Kang
  • Julien Touchèque
  • Luc Haine
  • Marco Drewes
  • Michele Lucente
  • Moritz von Stosch
  • Paul Smyth
  • Pedro Vaz
  • Philippe Ruelle
  • Pieter David
  • Pietro Vischia
  • Richard Ruiz
  • Roberta Volpe
  • Sebastien Clesse
  • sophie mathieu
  • Vincent Lemaitre
  • Zhengwen Liu