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| thesearesystems.substack.com
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.| thesearesystems.substack.com
You don't need to know the probabilities to make use of probability.| thesearesystems.substack.com
When feedback is involved, causation can actually imply a _lack_ of correlation.| thesearesystems.substack.com
Edwin Thompson Jaynes was a 20th-century physicist who wrote several texts on probability theory, espousing what some would describe as a very strongly opinionated view on the correct interpretation of probability.| thesearesystems.substack.com
Nothing says “I’m an engineer” like the humble decibel.| thesearesystems.substack.com
Why some systems -- as a consequence of their intrinsic dynamics -- require a control action that first pushes them the wrong way and makes things worse before it gets better.| thesearesystems.substack.com
