StanCon 2019 is open for registration. Two days of tutorials followed by two days of talks, open discussions, and statistical modeling.
- Submissions (Posters due August 15, rolling acceptance)
- 34 Accepted Presentations (opens new page)
- Scholarships (May 1 deadline)
- Registration (Rates go up 50% June 1)
- Tutorials: August 20-21, 2019
- Conference: August 22-23, 2019
- Conference Dinner: August 22, 2019
Tutorials: King’s College and Caius College.
Conference: West Road Concert Hall University of Cambridge 11 West Road, Cambridge CB3 9DP UK
Submission form here. All deadlines are midnight anywhere on the planet:
- Tutorial proposals from 1.5 hrs to 12 hrs are solicited. Deadline April 10.
- 15 minute presentation submissions for the conference are due by April 30, 2019. We are only requiring abstracts with the option of presentations without notebooks.
- Posters are due by August 15. Acceptance will be on rolling basis (4 days)
Basics of Bayesian inference and Stan. August 20, 21 all day. Instructors Jonah Gabry and Lauren Kennedy.
Description: In this tutorial we will first review some of the foundational concepts in Bayesian statistics that are essential background for anyone interested in using Bayesian methods in practice. Then we will introduce the Stan language and the recommended workflow for applied Bayesian data analysis by working through an example analysis together. Since we only have 2 days for this tutorial it will be beneficial for participants to have at least some previous experience with statistical modeling, but prior experience fitting Bayesian models is not a requirement. We will be interfacing with Stan from R, but users of Python and other languages/platforms can still benefit from the tutorial as all of the code we write in the Stan language (and all of the modeling techniques and concepts covered) can be used with any of the Stan interfaces.
Introduction to Stan for Programmers. No statistics background required. August 20, 21 mornings with afternoon exercises. R/Python based introduction to both Stan and statistics. Instructors Jonathan Auerbach, Breck Baldwin.
Description: This class is for those who don’t know statistics, extremely rusty with statistics or just want a gentle introduction to Bayesian modeling the Stan way. This tutorial will not cover as nearly much ground as the above class but will get you working with the basics of Bayesian modeling so that you can proceed to learn more. Most importantly we get you through the awkward ‘no idea phase’ of learning a new technology to having a basic understanding of how to work with the software. We will cover the mechanics of how Stan programs work, show simple examples of regression and pooling. We presuppose that you are comfortable with general programming concepts like subroutines, variable assignment and use of IDEs. Both the R and Python interfaces to Stan will be covered.
A Dive into Stan’s C++ Model Concept August 20, afternoon. Instructor Daniel Lee.
Description: This will be a discussion partially driven by the participants. We will cover things like:
- how the Stan language is translated to C++
- overview of the C++ model concept that’s generated
- how to use the math library for autodiff
- how to use the generated C++ model concept to write an inference algorithm in C++
- opportunities in efficient computing once the C++ is generated
Please be comfortable with C++ or at least be willing to stare at blocks of ugly code. Please have CmdStan installed.
Model assessment and selection August 21, afternoon. Instructor Aki Vehtari.
Description In this tutorial I will review estimation of predictive performance of models in M-open setting where we assume that none of the models is the true model. When we don’t trust our models, we can re-use data as a model-free proxy for the future data distribution. To adjust for data re-use we use leave-one-out cross-validation for exchangeable observations, K-fold cross-validation for group exchangeable observations, and m-step ahead validation for time series. I present how these performance estimates and related predictive distributions can be used for model comparison, averaging and checking. I discuss fast importance sampling approximation including useful diagnostics. Finally I discuss relations to alternative methods for model selection and weighting. Tutorial demonstrations are based on R with rstan, rstanarm, brms and loo packages.
Hierarchical Modeling with Stan. August 20, 21 mornings. Instructor Ben Goodrich.
Description: Hierarchical models are and should be the default way to conduct a Bayesian analysis. By hierarchical Bayesian models, we mean any generative process where the distribution for some unknown depends on one or more other unknowns. This includes instrumental variables, interaction terms, hierarchical shrinkage priors and many others. However, the most common instance of a hierarchical model is one where the generative process allows some of the parameters to vary by group, which can be conveniently specified using the syntax of the lme4 R package. In this tutorial we will start with estimating and interpreting estimates of hierarchical models using the stan_glmer function in the rstanarm R package, proceed to the brm function in the brms R package, and finish with some examples where participants modify or write code in the Stan language to specify a hierarchical model. Prerequisites: Some experience with R (not necessarily the aforementioned packages) and some experience with MCMC (not necessarily Stan).
Population and ODE-based models using Stan and Torsten. August 20, 21 afternoons. Instructors Charles Margossian, Yi Zhang.
This class covers techniques to build, fit, and criticize Bayesian models in pharmacometrics. When handling such models, we must address the following challenges: (i) the data generating process involves solutions to ODE systems; (ii) these ODEs are embedded in a complicated event schedule; (iii) the data comes from various sources, for instance various patients and studies, and the resulting models are hierarchical. Note that these properties are not specific to pharmacometrics, and arise in other fields such as epidemiology, geology, and econometrics. To accommodate a broad audience, we will keep the core concepts general, and review basic notions of pharmacometrics, so that participants from all fields can do the exercises.
The course reviews elementary techniques to solve ODEs in Stan, the efficient parametrization of hierarchical models, and within-chain parallelization. We also introduce Torsten, an extension of Stan for pharmacometrics, which allows us to seamlessly combine the above methods. We will give participants access to a cloud platform, so that users may use multiple cores when parallelizing Stan and Torsten. Original tutorial abstract here.
Please make your request here by the May 1, 2019 deadline.
Registration for tutorials and conference is here.
Pricing for the tutorials are £100 for Students, £275 for Junior Academics/Startups and £350 for Senior Academics/Industry.
Pricing for the conference is £100 for Students, £275 for Junior Academics/Startups and £350 for Senior Academics/Industry.
Due to very large reviewing load of papers we have extended the price raise to a 50% increase June 1, 2019.
If you require an invitation letter for visa or other purposes please fill out this form. We will email you a letter within 4 days. You must be registered to get the letter. Contact us if you need it sooner at stanConCambridge@mc-stan.org.
The overall schedule is two days of tutorials, August 20, 21 2019 followed by two days of conference, August 22, 23.
The rough conference schedule is 9am-5pm with invited talks being the first and last talks of the day. As speakers/paper acceptances come in we will update the schedule.
David Spiegelhalter, will speak on “Communicating Uncertainty about Facts, Numbers and Science”
Lauren Kennedy, will speak on “Out of sample prediction and the quest for generalization”
We have several options:
- Kings College Housing. Reference code is STANCONAUG2019. At £120 (single occupancy) and £180 (double occupancy) per night. Includes breakfast and is the venue closest to tutorials/conference. More information here.
Ibis Cambridge Central Station (3-star hotel) 2 Station Square, Cambridge, CB12GA, 01223 320960. A 25 min walk to King’s College (about 8 min by taxi) 35 min walk to West Road Concert Hall (about 9 min by taxi)
YHA Cambridge (hostel) 97 Tenison Road, CB1 2DN, 03453 719728. A 25 min walk to King’s College (about 8 min by taxi), 35 min walk to West Road Concert Hall (about 9 min by taxi)
For local travel to Cambridge from various UK locations here.
If you are staying in London before/after a train from King’s Cross Station or Liverpool Street Station is a good option. London Stansted Airport is the closest to Cambridge and there’s a direct train to Cambridge. Other airports are Heathrow and Gatwick. Train info here.
Buses from London Airports here.
We would like to thank our sponsors who both support conference costs but scholarships and Stan as a whole.