ketencimert / probprog-finalproject

Glicko2: A Probabilistic Chess Rating Framework

This is the final project by Ozan Adiguzel and Mert Ketenci for Machine Learning with Probabilistic Programming (Fall 2021) at Columbia University.

The goal of this project is to explore the Glicko2 rating system using Stan with a focus on inference methods. We fit the stochastic variance model described in (Glickman, 2001) using various inference algorithms.

Reference

• Glickman, M. E. (2001). Dynamic paired comparison models with stochastic variances. Journal of Applied Statistics, 28(6), 673-689.