POoor bugger will freeze to death trying to collect a lousy ten-dollar debt. What trials and tribulations that Rapp imposes on those who would do business with him!! It is for fie, I say!

In other news, it may not be generally known that the Kalman filter produces estimates of the true values of measurements and their associated calculated values by predicting a value, estimating the uncertainty of the predicted value, and computing a weighted average of the predicted value and the measured value. The most weight is given to the value with the least uncertainty. The estimates produced by the method tend to be closer to the true values than the original measurements because the weighted average has a better estimated uncertainty than either of the values that went into the weighted average.

From a theoretical standpoint, the Kalman filter is an algorithm for efficiently doing exact inference in a linear dynamical system, which is a Bayesian model similar to a hidden Markov model but where the state space of the latent variables is continuous and where all latent and observed variables have a Gaussian distribution (often a multivariate Gaussian distribution).

Thanks to this ingenious development we now have FM radios with phase-locked loops and can use reflected light hitting color sensors to generate surface velocity vectors of moving sensor platforms. How about them apples, Mom???