Welcome to the Bayesian Statistics course website!
Below you will find course information and below that the location, times and dates of the lectures.
All lectures will be given by Benjamin Wandelt.
The course will cover the foundations and applications of Bayesian Statistics. In this course you will learn all the central concepts of Bayesian statistics, starting from conditional probability. We will cover inference, prior selection, the Bayesian linear model, model comparison using the evidence, analytical solutions, and computational techniques such as Markov chain Monte Carlo, and implicit inference (simulation based inference) using deep learning.
The focus is on a clear and deep understanding of the concepts through an interactive format that alternates exposition of new ideas, worked examples, exercises, and practical applications.
The course will be given in English.
I will adapt it and the course material to respond to your specific needs and interests, so please think about what you would like to get out of this course and tell me on the first day!
Presentation of new material will be interspersed with exercises, as appropriate. You will be doing mini-projects in small groups (2-4) and present your results on the morning of the last day. I will suggest topics for mini-projects but am open to topics drawn from your research. You can code in your preferred language.
All my examples will use Mathematica, a very convenient mathematical modeling language. All Sorbonne U.~students have access to a free software license (Log on to http://mon..fr, then from Outils select Logiciels and click on the Mathematica link, listed uder Accès rapides) . If you have never used Mathematica please watch the video tutorials http://www.wolfram.com/broadcast/screencasts/handsonstart/ before the course (in English). See http://www.wolfram.com/support/learn/ for further information. An introduction to Mathematica in French is available at http://www.lct.jussieu.fr/pagesperso/toulouse/enseignement/cours_mathematica.pdf .
For recommended books and literature check out the Suggested Reading .
Course materials (Ben Wandelt's files) are available here. You can still find other lecturers's materials that were used in earlier versions of this class by using the links on the left.
All lectures will be held in the Planck room (salle Planck) at the
Institut d'Astrophysique de Paris
98 bis, boulevard Arago
75014 Paris
at the following times:
The course will be split over 2 consecutive weeks in May as follows:
ED127-AAIF Bayesian Statistics For Physical Scientists
Scheduled: 14 May 2024 at 09:30 to 13:00, CEST
ED127-AAIF Bayesian Statistics For Physical Scientists
Scheduled: 14 May 2024 at 15:30 to 18:00, CEST
ED127-AAIF Bayesian Statistics For Physical Scientists
Scheduled: 16 May 2024 at 14:30 to 18:30, CEST
ED127-AAIF Bayesian Statistics For Physical Scientists
Scheduled: 17 May 2024 at 09:30 to 13:00, CEST
ED127-AAIF Bayesian Statistics For Physical Scientists
Scheduled: 17 May 2024 at 14:30 to 18:00, CEST
ED127-AAIF Bayesian Statistics For Physical Scientists
Scheduled: 21 May 2024 at 09:30 to 13:00, CEST
ED127-AAIF Bayesian Statistics For Physical Scientists
Scheduled: 21 May 2024 at 15:30 to 18:00, CEST
ED127-AAIF Bayesian Statistics For Physical Scientists
Scheduled: 24 May 2024 at 09:30 to 13:00, CEST
ED127-AAIF Bayesian Statistics For Physical Scientists
Scheduled: 24 May 2024 at 14:00 to 17:30, CEST
©Benjamin Wandelt