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Introduction to Bayesian Computing

Description

COURSE OBJECTIVES
The students will be able to estimate a complex Bayesian model and to provide estimates uncertainty quantifications.

COURSE PREREQUISITES 
Students need to have passed the exam "Introduction to Data Science"

COURSE DESCRIPTION

  • Markov chains
  • Monte Carlo simulation
  • Importance sampling
  • Markov chain Monte Carlo (MCMC) methods
  • Adaptive MCMC
  • MCMC convergence diagnostics
  • Approximate Bayesian Computation

LEARNING METHODS
Weekly lectures will be complemented with tutorials and practicals  (with R statistical software)

 

EXAMINATION INFORMATION

A final exam worth 100% of the final grade.

 

REFERENCES
Students are requested to bring to class their own laptop with the R statistical software installed.

The material needed for this course will be provided during the course itself in the form of slides, papers and reference books.

Examples of reference books are:

  • Bayesian Essentials with R, Jean-Michel Marin, Christian P. Robert
  • Introducing Monte Carlo Methods with R, Christian Robert, George Casella
  • Markov Chain Monte Carlo in Practice, W.R. Gilks, S. Richardson, David Spiegelhalter
  • Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition, Dani Gamerman and Hedibert F. Lopes

People

 

Mira A.

Course director

Additional information

Semester
Spring
Academic year
2021-2022
ECTS
3
Language
English
Education
Master of Science in Computational Science, Elective course, Lecture, 2nd year
Master of Science in Computational Science, Elective course, 1st year