Bayesian Computation in Marketing

Interschool Reading Group


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, both within marketing, and in related fields including statistics and computer science. Each virtual meeting focuses on a specific paper, or a set of related papers, with the discussion led "presentation-style" by a group member. The group is organized by Ryan Dew and Longxiu Tian.


Membership is limited to junior faculty and Ph.D. students in Marketing, who have active research involving Bayesian computation and statistical (probabilistic) machine learning. All members must be willing to read papers and lead the discussion when it is their turn. Because we will focus on reading methodological papers, members should have familiarity with the fundamentals of Bayesian and probabilistic graphical modeling, Bayesian inference and computation (including MCMC and variational inference methods), and basic machine learning technologies like neural networks.

Interested in joining? Fill out this form.

Meeting Schedule and Topics

Possible topics include advances in inference methods and generative modeling, Bayesian nonparametrics and kernel methods, Bayesian neural networks, experimental design, reinforcement learning, recommendation systems, models of unstructured data, computational approaches to causal inference, and more. All meetings will take place virtually. A link will be sent to members before each meeting.

Upcoming readings / topics:

  1. The Blessings of Multiple Causes

  2. Multimodal VAEs

  3. Counterfactual Reasoning and Learning Systems

  4. Adapting Neural Networks for the Estimation of Treatment Effects

  5. Deep Neural Networks for Estimation and Inference

  6. Causal Inference for Recommender Systems

  7. An Overview of Robust Bayesian Analysis

  8. Robust Bayesian Inference via Coarsening

  9. Covariances, Robustness, and Variational Bayes

Past readings:


  • Counterfactual Inference for Consumer Choice Across Many Product Categories

  • Introduction to VAEs

  • Causal Effect Inference with Deep Latent Variable Models

  • Black-box Variational Inference

  • Variational Inference with Normalizing Flows

  • Decision-making with VAEs

  • Disentangled representation learning