Three Types of Bayesian Forgetting for Online Learning

If you’re running a Bayesian model in a non-stationary environment, you need to forget old data. The obvious approach – scale the precision matrix by a constant – has a failure mode called covariance windup. This post works through three forgetting rules, ending with one borrowed from adaptive control that dominates the others.

Notes on "Deep Bayesian Bandits Showdown" (Riquelme et al., 2018)

I probably overuse the normal-inverse-gamma posterior. Every time I build a bandit system, every time I need uncertainty quantification for sequential decisions, I end up back at conjugate linear regression.

Approximate Hierarchical Bayes for Online Decision-Making

Suppose you’re choosing a continuous value x and observing a noisy reward y. The reward depends on x through some unknown function f(x), and you’re making decisions repeatedly—learning as you go. This post explores how to build scalable Bayesian models for this problem using principled approximations.

An Empirical Bayes Approach to Churn Estimation

Everyone Wants a Churn Model

Rarely do I ever get asked to make churn estimates for someone who needs to bring the full power of a proportional hazards model to bear. Besides, the person asking for churn estimates doesn’t actually want to know “what is the probability someone churns eventually?” (Spoiler: it’s 1.)