I am trying to learn about the inference and maximization basically EM of the linear dynamic systems(Kalman filters for example) from Bishop's book of Pattern Recognition and Machine Learning. However, I am not being able to follow the derivations given in there.
I have got the basic idea of what Kalman filters are and what they are used for. However, I am a bit confused with the learning steps(basically the derivation of the equations and all). They seem a bit complicated. I have spent lot of time trying to figure them out. But I still have some issues. Can anyone suggest me where I can get the idea. Because in the book, they haven't given the details of how it is derived(the equations.
My question is how this is derived. It is a piece from the 2nd last image. I might be asking too much but I am really finding it difficult to get how this is derived. I would really appreciate if someone could give me some pointers