cond_logLik: Conditional log likelihood

In kidusasfaw/pomp: Statistical Inference for Partially Observed Markov Processes

Description

The estimated conditional log likelihood from a fitted model.

Usage

## S4 method for signature 'kalmand_pomp'
cond.logLik(object, ...)

## S4 method for signature 'pfilterd_pomp'
cond.logLik(object, ...)

## S4 method for signature 'bsmcd_pomp'
cond.logLik(object, ...)

Arguments

object	result of a filtering computation
...	ignored

Details

The conditional likelihood is defined to be the value of the density of

Yt | Y1,…,Y(t-1)

evaluated at Yt = yt*. Here, Yt is the observable process and yt* is the data, at time t.

Thus the conditional log likelihood at time t is

ell_t(theta)=log f[Yt = yt*t | Y1=y1*, …, Y(t-1)=y(t-1)*],

where f is the probability density above.

Value

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).

See Also

Other particle filter methods: bsmc2, eff.sample.size, filter.mean, filter.traj, mif2, pfilter, pmcmc, pred.mean, pred.var

kidusasfaw/pomp documentation built on May 20, 2019, 2:59 p.m.