jbconsulting

Why systems containing positive feedback loops will always be more prone to bouncing, wobbling, oscillating, jumping, vibrating, overshooting, and crashing than a passively stable design, no matter what you do to control them.

How the Kalman filter uses its knowledge of a system's dynamics to predict its next move, and why that derives from Bayesian probability rules

Answers for the curious.

A deep-dive

From time to time, I’ll post and comment on excerpts from the works of E. T. Jaynes that are particularly interesting or insightful. Here’s one that might change how you think about dealing with uncertainty. The Gaussian distribution (also known as the

In which we explore how the Kalman filter approximates Bayesian probability in its update process, and then push it beyond its limits as we tackle the "pathological" case of Cauchy-distributed noise via the lighthouse problem.

A deep-dive