Search for contacts, projects,
courses and publications

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 DESCRIPTION

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

 

LEARNING METHODS
There will be theoretical and applied (with R statistical software)  frontal and online lectures compliant with COVID-19 guidelines.

 

EXAMINATION INFORMATION
A project that will involve the analysis of data using the computational methodologies learned during the course will count for 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.

 

 

People

 

Mira A.

Course director

Additional information

Semester
Spring
Academic year
2020-2021
ECTS
3
Language
English
Education
Master of Science in Computational Science, Core course, Lecture, 1st year