Training Course Details

Bayesian Inference using Stan

Course Level: Intermediate
Despite the promise of big data, inferences are often limited not by the size of data but rather by its systematic structure.  Only by carefully modelling this structure can we take full advantage of the data — big data must be complemented with big models and the algorithms that can fit them.  Stan is a platform for facilitating this modelling, providing an expressive modelling language for specifying bespoke models and implementing state-of-the-art algorithms to draw subsequent Bayesian inferences.  The trainer for this course will be none other than Michael Betancourt, a core developer of Stan.

London, UK | July 17, 2019

£1,850.00 ex VAT per person
Venue Details:
July 17, 2019
9.00 am - 5.00 pm
3 days
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Limited availability

Course Details

Course Outline

In this three-day course we will introduce how to implement a robust Bayesian workflow in Stan, from constructing models to analyzing inferences and validating the underlying modelling assumptions.  The course will emphasize interactive exercises run through RStan, the R interface to Stan, and PyStan, the Python interface to Stan. We’re proud to say that Michael Betancourt, a core developer of Stan, will be leading this course. Michael is the principal research scientist with Symplectomorphic, LLC where he develops theoretical and methodological tools to support practical Bayesian inference. In addition to hosting tutorials and workshops on Bayesian inference with Stan he also collaborates on analyses in epidemiology, pharmacology, and physics, amongst others. 

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Course Structure

We will begin by surveying probability theory, Bayesian inference, Bayesian computation, and a robust Bayesian workflow in practice, culminating in an introduction to Stan and the implementation of that workflow.  With a solid foundation, we will continue with a discussion of regression modelling techniques along with their efficient implementation in Stan, spanning linear regression, discrete regression, and homogeneous and heterogeneous logistic regression.  Finally, we will discuss the basics of hierarchical modelling and, time permitting, multilevel modelling.

Prior Knowledge

The course will assume familiarity with the basics of linear algebra, calculus – including differentiation, integration, and Taylor series – and probability theory. For a self-contained introduction to the latter please review this probability case study and conditional probability case study.