Introduction to Bayesian modeling with (py)stan

Course Description

The aim of the course is to present the cornerstones of Bayesian modeling. It will (try to) answer the questions: – “What is so special about the Bayesian viewpoint?” – “Is there any way to profit from its finesses without going through all that mathematical hell?” We will try to construct convenient statistical models for selected toy and real-world examples, and estimate their parameters using (py)stan.

Course tools

• python
• scipy, numpy , pandas, pystan, arviz

Prerequisites

• Basics of probability theory and statistics Basics of python and numpy

Level of complexity of course Basics

Lecturer

Dr. Kamil Dedecius The Czech Academy of Sciences, Institute of Information Theory and Automation (researcher) / Czech Technical University in Prague, Faculty of Information Technology (assistant professor) Kamil Dedecius received a Ph.D. degree in engineering informatics from Czech Technical University, Prague, Czech Republic, in 2010. Since 2010, he has been with the Institute of Information Theory and Automation, Czech Academy of Sciences. His primary research interests include mainly Bayesian probability and statistics, in particular the estimation theory and its application in signal processing. His work has been recognized with the 2015 Otto Wichterle Award. Fields of interests: Statistical signal processing, Bayesian statistics, Estimation theory Contacts:dedecius@utia.cas.cz