I mentioned this Sloven proverb: "The past is more uncertain than the future, because it gets rewritten every day."
If you know anything about Kalman filtering, you know that it is about updating the future probability distribution as you get new information. Kalman smoothing is less know than Kalman filtering, although I don't understand why. Maybe because the smoothing things are too technical and smoothing is technically more complicated than filtering? Kalman smoothing is about updating not only the future, but also the past. As we learn more, not only we change our expectations about the future, but also we change our understanding of the past. How to incorporate this into the LTV? Best, Sabri _______________________________________________ pen-l mailing list [email protected] https://lists.csuchico.edu/mailman/listinfo/pen-l
