I am trying to figure out the derivation of Kalman filter based on Bayesian estimator. As we know, the assumption of Gauss-Markov model is used, then, the conditional distribution p(x(t)|Y(t-1))can be expressed in terms of the Gaussian distribution as
$C(y(t)|x(t))\sim \mathcal{N}(C(t)x(t),R_{vv}(t))$
$P(x(t)|Y(t-1))\sim \mathcal{N}(\tilde{x}(t|t-1),\tilde{P}(t|t-1))$
$P(y(t)|Y(t-1))\sim \mathcal{N}(\tilde{y}(t|t-1),\tilde{R_{ee}}(t))$
I am totally lost. Thank you in advance! By the way, this material orginates from the book "Modal Based Signal Processing (James V. Candy)" in Chapter 5 Page 296.