The estimated conditional log likelihood from a fitted model.
# S4 method for kalmand_pomp
cond.logLik(object, ...)# S4 method for pfilterd_pomp
cond.logLik(object, ...)
# S4 method for bsmcd_pomp
cond.logLik(object, ...)
result of a filtering computation
ignored
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.
When object is of class ‘bsmcd_pomp’ (i.e., the result of a bsmc2 computation), cond.logLik returns the conditional log “evidence” (see bsmc2).
The conditional likelihood is defined to be the value of the density of $$Y_t | Y_1,\dots,Y_{t-1}$$ evaluated at \(Y_t = y^*_t\). Here, \(Y_t\) is the observable process and \(y^*_t\) is the data, at time \(t\).
Thus the conditional log likelihood at time \(t\) is $$\ell_t(\theta) = \log f[Y_t=y^*_t \vert Y_1=y^*_1, \dots, Y_{t-1}=y^*_{t-1}],$$ where \(f\) is the probability density above.
Other particle filter methods: bsmc2,
eff.sample.size, filter.mean,
filter.traj, mif2,
pfilter, pmcmc,
pred.mean, pred.var