We are a small group of data scientists, statisticians, engineers and researchers interested in the practical applications of Bayesian statistics. Join us for friendly academic briefings, stories from real-world projects, and open discussion of Bayesian inference, tools, techniques and case studies.
We follow a conventional format of 1 or 2 presentations from volunteers in the group and/or invited experts, and general conversation and socialising afterwards. Think of us as your Bayesian self-help group in the Boston Area.
Some topics of interest are:
- Bayesian Data Analysis. - Probabilistic programming with PyMC3, STAN, others. - Model checking and comparison techniques. - Computational methods for approximate Inference. - Real-world examples of hierarchical models. - Bayesian methods for Machine Learning and deep learning. - Bayesian methods for Reinforcement Learning. - Non-linear and non-parametric models.
Call for presentations:
We provide a supporting environment to share your ideas and get feedback on your work. If you are interested in presenting, please submit presentation proposals at BostonBayesians@gmail.com.
Our December meeting will feature a talk from Katy McKeough, a PhD student at Harvard University. Katy will talk about novel bayesian models to predict athlete performance . Join us to learn more about this interesting topic and share your story with fellow bayesians.
Growth curves to predict athlete performance
It is often the goal of sports analysts, coaches and fans to predict athlete performance over time. Methods such as Elo, Glicko and Placket-Luce based ratings measure athlete skill based on results of competitions over time but have limited predictive strength on their own. Growth curves are often applied in the context of sports to predict future ability, but these curves are too simple to account for complex career trajectories. We propose a mixture of non-linear, mixed-effects growth curves to model the ratings as a function of athlete age and time. The mixture of growth curves allows for flexibility of the estimated shape of the career trajectories between athletes as well as between sports. We use the fitted growth curves to make predictions about the future career trajectory of an athlete in two ways: as a way to model how performance progresses over time in a multi-competitor scenario as an extension to the Plackett-Luce model and as a two-step method that can be applied to any rating system. We show how easily generalizable this method is to many different sports with specific examples from men's slalom and women's luge.
Katy McKeough is a fifth-year Ph.D. student in the Harvard University Statistics Department. She graduated from Carnegie Mellon University in 2015 with a B.S. in Physics & Statistics. Her research involves using advanced statistical models in applied settings including sports analytics and astrostatistics.
6:00pm: Meet and greet. Networking
6:30pm: Talk by Katy McKeough + Q&A
8:30pm: End of the event
This event is sponsored by QuantumBlack, a McKinsey Company.