Video lectures on the Kalman filter
I have released a series of video lectures on the Kalman filter, including an introduction to probability theory, Bayes’ theorem, minimum variance estimation, maximum likelihood and maximum a posteriori estimation. We start with a gentle introduction to probability theory (probability spaces, random variables, expectation, variance, density functions, etc) and move on to conditioning, which is a notion of central importance in estimation theory.
Continue reading →Cholesky updates of A’A
Updating the Cholesky factorization of 
Mathematix 