Introduction to Bayesian Inference with Stan with Michael Betancourt

Mon, April 08 2019, 9:00 AM - Thu, April 11 2019, 5:00 PM [EST]

4200 Fifth Avenue, Pittsburgh, PA, 15260, United States

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General Admission (3-day) Partial Approval -$2,500.00

Includes first three days of the course, from Monday April 8 to Wednesday April 10.

General Admission with Advanced Techniques (4-day) Partial Approval - $3,300.00

All four days of the course, from Monday April 8 to Thursday April 11.

Advanced Techniques Only (1-day) Partial Approval -$950.00

Admission to the advanced techniques day on Thursday, April 11.

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Event Information

Mon, April 08 2019, 9:00 AM - Thu, April 11 2019, 5:00 PM [EST]

About the Event

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 (with an elective fourth day) 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 Bayesian inference, Bayesian computation, and a principled introduction to Stan and the Stan modeling language.  With a solid foundation we will move onto to the elements of a robust Bayesian workflow in practice and then continue to regression, hierarchical, and multilevel modeling techniques.

On the fourth day we will cover advanced modeling techniques, specifically Gaussian processes and Bayesian sparse regression.

This course was made possible with support from the Dietrich School of Arts and Sciences and TextLab at the University of Pittsburgh.

Prerequisites 

The course will assume familiarity with the basics of calculus, linear algebra, and probability theory.  For a self-contained introduction to the latter please review my probability case study and conditional probability case study.

In order to participate in the interactive exercises attendees must provide a laptop with the latest version of RStan or PyStan installed.  Please verify that you can run the 8schools model as discussed in the RStan Quick Start Guide or the PyStan Quick Start Guide and report any installation issues on the Stan Forums as early as possible.

Venue Details

The Cathedral of Learning lies at the center of the University of Pittsburgh campus and a 20 minute busride from downtown Pittsburgh.  Lunch and refreshments will be provided.

Cancelation Policy

Cancellations with refunds of the registration cost will be honored until March 8, 2018.  After March 8, 2018 cancellations will be considered only in the event of emergencies.

Testimonials

“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 

 

“Stan is the cream of the crop platform for doing Bayesian analysis and is particularly appealing because of its open source nature. The programming language and algorithms are well designed and thought out. With that said, Stan has a very steep learning curve requiring lots of hours to get up to speed on your own. I have been to two training courses taught by Dr. Michael Betancourt and took an opportunity to have some consulting time. These sessions have proven invaluable to improve my use of Stan, increased my learning and usage rate, and informed me how to diagnose and detect issues that will inevitable will arise.”

-Robert Johnson, Corporate R&D, Procter & Gamble

 

“The workshop at MIT led by Michael Betancourt was a fun and very useful introduction to Stan. Mike worked with us to customize the lectures to our interests, he presented the material in an engaging and accessible way, and the physicists who attended, many of whom had never used Stan before, left with the resources to begin developing our own analyses. Mike’s background in physics makes him an especially effective teacher for scientists. The coding exercises were thoughtfully developed to progress in complexity and were well-integrated into the course; having such useful exercises was critical for participants to successfully internalize the concepts presented in the lectures.”

 -Elizabeth Worcester, Associate Physicist, Department of Physics, Brookhaven National Laboratory

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About the Organizer

Michael Betancourt is a research scientist with Symplectomorphic, LLC 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, amongst others. Before moving into statistics, Michael earned a B.S. from the California Institute of Technology and a Ph.D. from the Massachusetts Institute of Technology, both in physics.

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