Seminar Bayesian computation: state of the art and recent developments
Bayesian
computation mainly revolves around computation of the posterior
distribution. Often these cannot be computed analytically in closed
form, especially for big and complex datasets. Hence, approximations
became more and more popular. Recent decades have seen enormous
improvements in computational inference for statistical models, with
both theoretical and algorithmic innovations opening new opportunities
to practitioners.
In this seminar, we will discuss state of the art
methods like MCMC algorithms but also recent developments in approximate
Bayesian techniques, such as ABC or variational Bayes. We will aim for
both: A good theoretical understanding of the presented methods as well
as showing how helpful such methods are in Economics, Life Sciences, and
other fields.
Registration from 1 to 15 August 2020 via moodle.
Required prior lecture: "Introduction to Statistical and Machine Learning".
- Kursverantwortliche/r: Prof. Dr. Nadja Klein
- Kursverantwortliche/r: Lucas Kock