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carlopecoraro2 ★ 2.4k

An introduction to computational Bayesian methods


Where: Free University (FU) Berlin (Germany)

When: 25-29 March 2019

Instructors: Prof. Shravan Vasishth and Bruno Nicenboim (University of Potsdam, Germany)

In this course, we seek to cover this gap, by providing a relatively accessible and technically non-demanding introduction to the basic workflow for fitting different kinds of linear models using Stan. To illustrate the capability of Bayesian modeling, we will use the R package RStan and a powerful front-end R package for Stan called brms.

After completing this course, the participant will have become familiar with the foundations of Bayesian inference using Stan (RStan and brms), and will be able to fit a range of multiple regression models and hierarchical models, for normally distributed data, and for log-normal, poisson, multinomial, and binomially distributed data. They will know how to calibrate their models using prior and posterior predictive checks; they will be able to establish true and false discovery rates to validate discovery claims, and to carry out model comparison using cross-validation methods, and Bayes factors

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Bayesian-Statistics R Stan • 693 views

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