To register for this course, and for payment and location details : please do not use this Meetup page. Go to : https://presciient.com/event/stan-syd-feb-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 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 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. Time permitting, we will consider the practical implementation of advanced modeling techniques at the state of the art of applied statistics research—such as Gaussian process priors and the horseshoe prior.
Prerequisites
The course will assume familiarity with the basics of calculus and linear algebra.
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 the Stan forum as early as possible.
The instructor: Michael Betancourt
Michael Betancourt is a research scientist with Symplectomorphic, where he develops theoretical and methodological tools to support practical Bayesian inference. He is also a core developer of Stan, where he implements and tests these tools. In addition to hosting tutorials and workshops on Bayesian inference with Stan, he also collaborates on analyses in epidemiology, pharmacology, and physics, among others. Before moving into statistics, Michael earned a BS from the California Institute of Technology and a PhD from the Massachusetts Institute of Technology, both in physics. Find out more at Michael’s website. https://betanalpha.github.io/consulting/
To read what students are saying about Michael’s courses, please scroll to the bottom of his consulting page. https://betanalpha.github.io/consulting/
