Introduction to Bayesian Computing

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.