Description Usage Arguments Value
Kalman_smoother returns X and likelihood. The penalized likelihood is the likelihood minus the sum-of-squares of the measurement update. This is used as the fitness function in genetic algorihm.
1 | penalized_likelihood(y, u, v, theta.vec, lambda)
|
y |
Observation matrix (may need to be normalized and centered before hand) (q rows, T columns) |
u |
Input matrix for the state equation (m_u rows, T columns) |
v |
Input matrix for the output equation (m_v rows, T columns) |
theta.vec |
a vector of parameter elements (i.e, the vectorized version of theta
in |
lambda |
weight of the penalty |
The penalized likelihood (a real number)
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