Coursebooks

Bayesian Computation

MATH-435

Lecturer(s) :

Dehaene Guillaume Philippe Ivan Joseph

Language:

English

Summary

This course aims at giving a broad overview of Bayesian inference, highlighting how the basic Bayesian paradigm proceeds, and the various methods that can be used to deal with the computational issues that plague it. This course represents a 70-30 split of practice versus theory.

Content

Key results that will be presented during the class:

 

Exercise sessions will be focused on implementation of the methods presented during the class, and on practical aspects of Bayesian data analysis.

The evaluation consists of an oral presentation on a programming project carried out by the student during the semester.

 

Learning Prerequisites

Required courses

A master's level understanding of real analysis, linear algebra, statistics and of probability theory is required for this course.

Learning Outcomes

By the end of the course, the student must be able to:

Teaching methods

Lecture ex cathedra, exercises in class, homework

Expected student activities

Evaluation is based on a programming project.

Assessment methods

Evaluation is based on a programming project.

Supervision

Office hours No
Assistants Yes
Forum No

Resources

Bibliography

C. Bishop, Pattern Recognition and Machine Learning

K. Murphy, Machine Learning: A Probabilistic Perspective

C. Robert, The Bayesian choice

Ressources en bibliothèque

In the programs

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     
Under construction
 
      Lecture
      Exercise, TP
      Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German