Description Usage Arguments Details Value See Also

The estimated conditional log likelihood from a fitted model.

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, ...)
``` |

`object` |
result of a filtering computation |

`...` |
ignored |

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.

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()`

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