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.
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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.
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.