Introduction to Bayesian modeling

Introduction to Bayesian modeling

Course description

The course will cover the basic principles of Bayesian modeling, namely the probabilistic model, the prior information, and the derivation of the posterior information by means of the Bayes’ theorem. The probabilistic programming principles will be described and demonstrated on selected examples using the python programming language and the PyStan package, providing easy interfacing to stan. Course tools Python (pystan, matplotlib, arviz, scipy, numpy) Prerequisites
  • Elementary knowledge of linear algebra (vectors, matrices, products)
  • Elementary knowledge of probability and statistics (distributions, expected value)
Level of complexity of course Basics


Dr. Kamil Dedecius Kamil Dedecius received a Ph.D. degree in Engineering Informatics from the Czech Technical University in Prague, Czech Republic, in 2010. Since then, 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. Since 2013 he focuses on the theory of fully distributed estimation in diffusion networks. Fields of interests: Bayesian modeling, estimation theory, sequential (online) modeling, distributed estimation

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