cond_logLik function

Conditional log likelihood

The estimated conditional log likelihood from a fitted model. methods

## S4 method for signature 'kalmand_pomp'
cond_logLik(object, ..., format = c("numeric", "data.frame"))

## S4 method for signature 'pfilterd_pomp'
cond_logLik(object, ..., format = c("numeric", "data.frame"))

## S4 method for signature 'wpfilterd_pomp'
cond_logLik(object, ..., format = c("numeric", "data.frame"))

## S4 method for signature 'bsmcd_pomp'
cond_logLik(object, ..., format = c("numeric", "data.frame"))

## S4 method for signature 'pfilterList'
cond_logLik(object, ..., format = c("numeric", "data.frame"))

Arguments

• object: result of a filtering computation
• ...: ignored
• format: format of the returned object

Returns

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).

Details

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

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

where $f$ is the probability density above.

See Also

More on sequential Monte Carlo methods: bsmc2(), eff_sample_size(), filter_mean(), filter_traj(), kalman, mif2(), pfilter(), pmcmc(), pred_mean(), pred_var(), saved_states(), wpfilter()

Other extraction methods: coef(), covmat(), eff_sample_size(), filter_mean(), filter_traj(), forecast(), logLik, obs(), pred_mean(), pred_var(), saved_states(), spy(), states(), summary(), time(), timezero(), traces()