I've derived the x + Ky form for the univariate kalman filter.
I completely reordered material, cutting about 10 pages (pdf)
of material. I made the connection between the bayesian form
and orthogonal form more explicit.
Probably there are a lot of grammatical errors, but I wanted to get
these checked in.
I also altered the css - mainly the font.
Added the likelihood equations/form from the discrete bayes
chapter to better tie in that form of reasoning. then I converted
the 1d equations to the orthogonal projection form to show how
the Kalman gain is computed and where the residual comes from
computationally. This should make the full KF equations much more
approachable.
Pretty happy with it now. Needs copy editing, and probably an
easier introduction to convey the basic idea. Moved from a class
based approach to a procedural approach, and I like that very much.
This code did not work for Python 2.x becaus I was not
importing from future. While I was altering all the files
I updated the header to include license information.
