The estimated conditional log likelihood from a fitted model.
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result of a filtering computation
The conditional likelihood is defined to be the value of the density of
Yk | Y1,…,Y(k-1)
evaluated at Yk = yk*. Here, Yk is the observable process, and yk* the data, at time t_k.
Thus the conditional log likelihood at time t_k is
ell_k(theta)=log f[Yk = yk* | Y1=y1*, …, Y(k-1)=y(k-1)*],
where f is the probability density above.
The numerical value of the conditional log likelihood.
Note that some methods compute not the log likelihood itself but instead a related quantity.
To keep the code simple, the
cond.logLik function is nevertheless used to extract this quantity.
object is of class ‘bsmcd_pomp’ (i.e., the result of a
cond.logLik returns the conditional log “evidence” (see
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