In a previous question, I inquired about fitting distributions to some non-Gaussian empirical data.
It was suggested to me offline, that I might try the assumption that the data is Gaussian and fit a Kalman filter first. Then, depending on the errors, decide if it is worth developing something fancier. That makes sense.
So, with a nice set of time series data, I need to estimate several variable for a Kalman filter to run.
(Sure, there is probably an R package somewhere, but I want to actually learn how to do this myself.)