xtbf{K}_t = \textbf{P}_t \textbf{C}' (\textbf{C P}_t \textbf{C}' + \textbf{R})^{-1}$$
$$\textbf{F}_t = \textbf{F}_t + \textbf{K}_t (\textbf{x}_t - \textbf{C F}_t)$$
$$\textbf{P}_t = \textbf{P}_t - \textbf{K}_t\textbf{C P}_t$$</p>
<p>If a row of the data is all missing the measurement update is skipped i.e. the prediction becomes the filtered value. The log-likelihood is
computed as
$$1/2 \sum_t \log(|St|)-e_t'S_te_t-n\log(2\pi)$$
where \(S_t = (C P_t C' + R)^{-1}\) and \(e_t = x_t - C F_t\) is the predictio