Contact Info
Department of Applied Mathematics
University of Waterloo
Waterloo, Ontario
Canada N2L 3G1
Phone: 5198884567, ext. 32700
Fax: 5197464319
PDF files require Adobe Acrobat Reader
MS Teams
Supranta Sarma Boruah’s  Applied Math, University of Waterloo
Bayesian data analysis with Markov Chain Monte Carlo (MCMC) methods
Bayesian data analysis techniques have found a widespread applications in the scientific fields over the last few decades. In particular. Markov Chain Monte Carlo methods are widely used for statistical inference.
In this seminar series, I will introduce the concepts of Bayesian data analysis, in particular, using MCMC methods. I will start with the familiar example of linear regression, interpreted in the Bayesian language and then graduate to examples with real cosmological data. In the second part of the series, I will introduce MCMC algorithms beyond the usual MetropolisHastings algorithm, which are beginning to be used more extensively and diagnostic tests useful for analyzing properties of the MCMC runs. Finally, in the third part of the series, I will introduce the concept of Bayesian hierarchical modelling, which through its probabilistic modelling captures known relations between different variables.
The sessions will be a handson. Bringing laptops with jupyter notebook installed is encouraged.
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Contact Info
Department of Applied Mathematics
University of Waterloo
Waterloo, Ontario
Canada N2L 3G1
Phone: 5198884567, ext. 32700
Fax: 5197464319
PDF files require Adobe Acrobat Reader
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