Dates: Wed, November 14 2018 - Fri, November 16 2018
Location: New York, New York
Instructors: Michael Betancourt and Sean Talts
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. 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.
The course will assume familiarity with the basics of calculus and linear algebra.
In order to participate in the interactive exercises attendees must provide a laptop with the latest version of RStan (https://cran.r-project.org/web/packages/rstan/index.html) or PyStan (http://pystan.readthedocs.io/en/latest/) installed. Users are encouraged to report any installation issues at http://discourse.mc-stan.org as early as possible.
“We had a brilliant 3-day course at trivago with Michael Betancourt! The first day was filled with a very strong theoretical foundation for statistical modelling/decision making, followed by a crash course on MCMC and finished off with practical examples on how to diagnose healthy model fitting. In the 2nd and 3rd days we learned about many different types of hierarchical/multi-level models and spent most of the time practicing how to actually create and fit these models in Stan.
Michael is both a very engaging teacher, a very knowledgeable statistical modeller and, of course, a Stan master. This course has opened up new ways for us at trivago to gain better insights from our data through Stan models that fit our needs.”
Data Scientists in the Automated Bidding Team, trivago
“The 1-day training course provided a great introduction to Bayesian models and their implementation in the Stan language. The practical focus really helped jumpstart our transition to Bayesian methods, and the slides, recorded lecture, and exercises also provide a great resource for new group members.”
Stanley Lazic, Associate Director in Statistics and Machine Learning, AstraZeneca
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