17. July 2019 - 8:00
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RStan with Michael Betancourt | London | Wednesday, 17. July 2019

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 modeling this structure can we take fully 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 modeling, providing an expressive modeling language for specifying bespoke models and implementing state-of-the-art algorithms to draw subsequent Bayesian inferences.

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 modeling assumptions.  The course will emphasize interactive exercises run through RStan, the R interface to Stan, and PyStan, the Python interface to Stan.

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 modeling 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 modeling and, time permitting, multilevel modeling.


The course will assume familiarity with the basics of linear algebra and calculus, including differentiation, integration, and Taylor series.

In order to participate in the interactive exercises attendees must provide a laptop with the latest version of RStan () or PyStan () installed.  Users are encouraged to report any installation issues at  early as possible

Michaels web page     
twitter account            @betanalpha
Stan Core developer   

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