Really Fast Bayesian Linear Regression
A little performance vignette. I measured the draw and update of a Bayesian linear regression against how fast one core can actually go, and found which scipy routines get there and which don’t.
Posts tagged "Machine Learning"
5 items found
Notes on "From Predictions to Decisions" (Wen et al., 2022)
Wen et al. show two forecasters with identical marginal predictions can make arbitrarily different decisions. A disjoint contextual bandit (one model per arm) is exactly their independence forecaster. In conjugate linear models the joint is exact and free, and partial pooling through a shared component gets it back.
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.