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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