|MATLAB MEX Function Reference|
The measurement (or observation) equation can be written
The transition (or state) equation is denoted as a first-order Markov process of the state vector.
The KALCVF function computes the one-step prediction and the filtered estimate , together with their covariance matrices and , using forward recursions. You can obtain the k-step prediction and its covariance matrix with the KALCVF function. The KALCVS function uses backward recursions to compute the smoothed estimate and its covariance matrix when there are T observations in the complete data.
The KALDFF function produces one-step prediction of the state and the unobserved random vector as well as their covariance matrices. The KALDFS function computes the smoothed estimate and its covariance matrix .
KALCVF performs covariance filtering and prediction
KALCVS performs fixed-interval smoothing
Getting Started with State Space Models
Kalman Filtering Example 1: Likelihood Function Evaluation
Kalman Filtering Example 2: Estimating an SSM Using the EM Algorithm
 Harvey, A.C., Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge: Cambridge University Press, 1991.
 Anderson, B.D.O., and J.B. Moore, Optimal Filtering, Englewood Cliffs, NJ: Prentice-Hall, 1979.
 Hamilton, J.D., Time Series Analysis, Princeton, 1994.
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