How to turn your time estimates into reliable predictions — and communicate them.
Shelter costs are rising rapidly in the US, but the situation in Canada is more precarious
Exploring the relationship between Kalman filtering and optimal frequency domain filtering.
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
Why systems containing positive feedback loops will always be more prone to bouncing, wobbling, oscillating, jumping, vibrating, overshooting, and…
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…
A deep-dive
You don't need to know the probabilities to make use of probability.
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