# cond_logLik: Conditional log likelihood In pomp: Statistical Inference for Partially Observed Markov Processes

## Description

The estimated conditional log likelihood from a fitted model.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```## S4 method for signature 'kalmand_pomp' cond.logLik(object, ...) ## S4 method for signature 'pfilterd_pomp' cond.logLik(object, ...) ## S4 method for signature 'wpfilterd_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

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.

## 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`).

More on particle-filter based methods in pomp: `bsmc2()`, `eff.sample.size()`, `filter.mean()`, `filter.traj()`, `kalman`, `mif2()`, `pfilter()`, `pmcmc()`, `pred.mean()`, `pred.var()`, `saved.states()`, `wpfilter()`