I am currently available to consult on Bayesian analyses, from the implementation of a Bayesian workflow into existing analysis pipelines to the development of sophisticated Bayesian models bespoke to a particular application.

Additionally I am available to host courses covering introductory or advanced Bayesian modeling with Stan. My courses are highly interactive, with exercises demonstrating robust Bayesian workflow and modeling techniques and in either R or Python environments. Courses run from 1 to 5 days and the curriculum is customizable. Previous clients include top research universities, government research organizations, pharmaceutical companies, consumer goods companies, and e-commerce companies.

Please contact me for any inquiries.

Testimonials from Previous Clients

“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 Stan tutorial workshop was a great experience for both students and faculty alike. Michael provided through and in-depth lectures on the foundations of Bayesian inference. These lectures were then followed by coding exercises that provided real-world exercises of the material. The combination of lectures and exercises provided a thorough approach to the material. The course is highly recommended.”

Joseph Formaggio, Associate Professor of Physics, Massachusetts Institute of Technology

“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

“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

“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

“We are very grateful for the excellent course which Mike gave at Newcastle University. Mike’s passion for Bayesian inference and the Stan modelling language and software was clear from every aspect of his delivery of the course. He managed to provide a high-level, clearly-reasoned presentation of the basics of Bayesian inference and Bayesian computing, before carefully leading us through the core constructs of the Stan modelling language, and the key ideas when writing Stan models for inference. This included a number of tricks which have been indispensable! I now regularly use Stan to fit models in my own work. I can’t praise Mike highly enough.”

Sarah Heaps, Teaching Fellow in Statistics, Newcastle University