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. Probabilistic programming languages like Stan facilitating this modeling, allowing us to implement bespoke models and while providing state-of-the-art algorithms to compute Bayesian inferences.
These courses present a series of advanced Bayesian modeling techniques and their implementation in principled Bayesian workflow, including discussions of prior modeling, inferential degeneracies, and more. Each course module incorporates interactive exercises run through RStan, the R interface to Stan, and PyStan, the Python interface to Stan.
Prerequisites
The course is aimed at current Stan users and will assume familiarity with the basics of calculus, linear algebra, probability theory, probabilistic modeling and statistical inference, and Stan.
Attendees are strongly encouraged to review both the probability theory chapters under "Part I" and the modeling and inferences chapters under "Part II" of https://betanalpha.github.io/writing/.
In order to participate in the exercises attendees must have a computer with RStan 2.32.3 and at least R 4.3.2 (https://cran.r-project.org/web/packages/rstan/index.html) or PyStan 3.7 and at least Python 3.9 (https://pystan2.readthedocs.io/en/latest/) installed. Please verify that you can run the 8 schools model as discussed in https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started or https://pystan.readthedocs.io/en/latest/getting_started.html and report any installation issues at http://discourse.mc-stan.org as early as possible. While the exercises cannot be run directly with other Stan interfaces such as CmdStanR and CmdStanPy they are relatively straightforward to translate.
Module Details
ach module consists of a live lecture followed by exercises for attendees to complete individually before a final live review session. The modules are offered in parallel morning and afternoon sessions for scheduling flexibility; the morning and afternoon sessions of each module cover the same material and attendees are recommended to attend only one session.
The lecture and review will take place on Whereby which does not require any account registration from attendees. Both will be recorded and the recordings will be available for course attendees to download shortly after each lecture/review concludes. Moreover the lecture slides will be available to download after the lecture concludes, allowing attendees to follow along remotely if convenient.
By participating in a course you consent to having any video and audio you transmit recorded and shared with the rest of the course attendees. Not sharing your video and audio will not prevent you from viewing the sessions or asking questions through the interactive chat.
In between the lecture and review session attendees will be able to discuss the lecture and the exercises with each other on a dedicated Discord server.
Module 1: Mixture Modeling
Module 1 introduces mixture modeling and its many applications to Bayesian inference, with an emphasis on using mixture models to model intertwined data generating processes. This module discusses efficient and numerically stable implementations of these methods and the inferential problems that arise when mixing together redundant models.
Morning Session
First Lecture: Monday August 18 10:00 EDT - 12:00 EDT
Exercise Review: Thursday August 21 10:00 EDT - 11:30 EDT
Afternoon Session
First Lecture: Monday August 18 15:00 EDT - 17:00 EDT
Exercise Review: Thursday August 21 15:00 EDT - 16:30 EDT
Module 2: Survival Modeling
The second module presents survival modeling from a Bayesian modeling perspective, with an explicit data generating interpretations of the hazard and survival functions. This module will cover the implementation of survival models with basic hazard functions, including exponential and Weibull models, as well as techniques for accommodating left, right, and internal censoring. Note that this module will not cover spine-based survival models.
Morning Session
First Lecture: Monday August 25 10:00 EDT - 12:00 EDT
Exercise Review: Thursday August 28 10:00 EDT - 11:30 EDT
Afternoon Session
First Lecture: Monday August 25 15:00 EDT - 17:00 EDT
Exercise Review: Thursday August 28 15:00 EDT - 16:30 EDT
Module 3: Pairwise Comparison Modeling
The third module discusses general pairwise comparison modeling, including the special cases of Bradley-Terry/Elo and item response theory modeling. A particular emphasis is placed on the inferential degeneracies inherent to these models and productive management strategies.
Morning Session
First Lecture: Monday September 15 10:00 EDT - 12:00 EDT
Exercise Review: Thursday September 18 10:00 EDT - 11:30 EDT
Afternoon Session
First Lecture: Monday September 15 15:00 EDT - 17:00 EDT
Exercise Review: Thursday September 18 15:00 EDT - 16:30 EDT
Module 4: Ordinal Modeling
Finally Module 4 presents techniques for modeling ordinal outcomes, such as those arising from surveys and rating systems. This module specifically focuses on how ordinal behavior varies across different contexts and discusses cut point techniques for modeling these systematic variations, as well as potential inferential degeneracies and management strategies.
Morning Session
First Lecture: Monday September 22 10:00 EDT - 12:00 EDT
Exercise Review: Thursday September 25 10:00 EDT - 11:30 EDT
Afternoon Session
First Lecture: Monday September 22 15:00 EDT - 17:00 EDT
Exercise Review: Thursday September 25 15:00 EDT - 16:30 EDT
Receipts and Certificates
Official receipts and certificates of completion will be available upon request.
Cancellation Policy
Cancellations will be considered only in the event of emergencies. Those not able to attend the modules due to unexpected scheduling conflicts will be able to follow along with the recordings and Discord discussion groups. If you have any questions then don't hesitate to contact me.
Discount Policy
If you are interested in purchasing more than 12 tickets then contact me about group discounts.
Unfortunately at this time I am unable to accommodate general academic discounts, but depending on availability I may be able to accommodate a few discounted tickets with priority given to black, indigenous people of color in high-income countries or those from low and middle-income countries. If you are interesting in inquiring about these opportunities then don't hesitate to contact me.
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
