cond_logLik | R Documentation |

The estimated conditional log likelihood from a fitted model.

```
## 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"))
```

`object` |
result of a filtering computation |

`...` |
ignored |

`format` |
format of the returned object |

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

`Y(t_k) | Y(t_1),\dots,Y(t_{k-1})`

evaluated at `Y(t_k) = y^*_k`

.
Here, `Y(t_k)`

is the observable process, and `y^*_k`

the data, at time `t_k`

.

Thus the conditional log likelihood at time `t_k`

is

`\ell_k(\theta) = \log f[Y(t_k)=y^*_k \vert Y(t_1)=y^*_1, \dots, Y(t_{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 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()`

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