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Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan (BDRS01)

3 December 2018 - 7 December 2018

Course Overview:

This course provides a general introduction to Bayesian data analysis using R and the Bayesian probabilistic programming language Stan. We begin with a gentle introduction to all the fundamental principles and concepts of Bayesian data analysis: the likelihood function, prior distributions, posterior distributions, high posterior density intervals, posterior predictive distributions, marginal likelihoods, Bayes factors, etc. We will do this using some simple probabilistic models that are easy to understand and easy to work with. We then proceed to more practically useful Bayesian analyses, starting with general linear models, followed by generalized linear models, including logistic regression and Poisson regression, followed by multilevel general and generalized linear models. For these analyses, we will use real world data sets, and carry out the analysis with Stan using the brms interface to Stan in R. With each example, we will explore general concepts such as model checking and improvement using posterior predictive checks, and model evaluation using cross-validation, WAIC, and Bayes factors. In the final part of the course, we will delve into some more advanced topics: understanding Markov Chain Monte Carlo in depth, Gaussian process regression, probabilistic mixture models.

To find out more or to book online via our sister company (PS statistics) use the link below…

Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan (BDRS01)

Details

Start:
3 December 2018
End:
7 December 2018

Venue

PS statistics head office
53 Morrison Street
Glasgow, G5 8LB United Kingdom
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