Boston Stan meetup 12 June!

May 15, 2018
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(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

Shane Bussmann writes to announce the next Boston/Camberville Stan users meetup, Tuesday, June 12, 2018, 6:00 PM to 9:00 PM, at Insight Data Science Office, 280 Summer St., Boston:

To kick things off for our first meetup in 2018, I [Bussman] will give a talk on rating teams in recreational ultimate frisbee leagues. In this talk, I show how a Bayesian framework offers a simple, clear path to rating teams that has a number of benefits relative to alternative, more heuristic-based approaches. Specifically, the Bayesian framework (1) transparently incorporates strength of schedule into the ratings; (2) allows the use of priors to account for the fact that teams self-select into one of three divisions (i.e., skill levels); (3) makes model validation straightforward; and (4) can lead to fun topics like quantitatively predicting the outcome of the end-of-season tournament. I will present a Stan model that implements this Bayesian framework and apply it to data from the local ultimate frisbee league run by the Boston Ultimate Disc Alliance. I use ScalaStan (an open-source Scala DSL for Stan) to build and run the model and Evilplot (an open-source data visualization library written in Scala) to make plots.

Special thanks to CiBO Technologies (http://www.cibotechnologies.com/) for sponsoring this meetup and to Insight Data Science (https://www.insightdatascience.com/) for hosting the event!

There are interesting features in the above abstract. Adjusting for strength of schedule is the bread and butter of probabilistic item-response models. But we also see adjustment for self-selection, and validation, two items that are important for statistical modeling and workflow but often get overlooked. Also possibly of interest is the use of Scala.

Too bad Phil and Aki (not to mention David Mackay) can’t make this one.

The post Boston Stan meetup 12 June! appeared first on Statistical Modeling, Causal Inference, and Social Science.



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