About

The Bayesian Computation in Marketing Interschool Reading Group brings together academic marketers with research interests in Bayesian computation and statistical machine learning to read and discuss recent advances in methodology, within marketing and in related fields, including statistics and computer science. Each virtual meeting focuses on a specific paper, book chapter, or a set of related papers with the discussion led "presentation-style" by a group member. The group was founded in 2020 by Ryan Dew and Longxiu Tian, and is currently organized by Shin Oblander and Anya Shchetkina.

Membership

Membership is limited to junior faculty and Ph.D. students, who are actively interested in research involving Bayesian computation and probabilistic machine learning in marketing. All members must be willing to read papers, contribute to the discussion of papers presented by other members, and lead the discussion when it is their turn. Because we focus on reading methodological papers, members should have familiarity with the fundamentals of Bayesian modeling, inference, and computation, as well as basic machine learning like neural networks. 

Interested in joining? Send us an email or fill in the form below!

Meeting Schedule and Topics

We meet biweekly, currently on Tuesdays 1:30PM Eastern Time / 10:30AM Pacific Time. All meetings are held virtually, with a link being sent to members prior to each meeting. Examples of previous topics include: Bayesian causal inference, Bayesian nonparametrics, reinforcement learning and bandits, Bayesian deep learning, mechanistic interpretability, and scalable inference. Suggest new topics in the form below!