pred_mean {pomp} | R Documentation |
Prediction mean
Description
The mean of the prediction distribution
Usage
## S4 method for signature 'kalmand_pomp'
pred_mean(object, vars, ..., format = c("array", "data.frame"))
## S4 method for signature 'pfilterd_pomp'
pred_mean(object, vars, ..., format = c("array", "data.frame"))
Arguments
object |
result of a filtering computation |
vars |
optional character; names of variables |
... |
ignored |
format |
format of the returned object |
Details
The prediction distribution is that of
X(t_k) \vert Y(t_1)=y^*_1,\dots,Y(t_{k-1})=y^*_{k-1},
where X(t_k)
, Y(t_k)
are the latent state and observable processes, respectively, and y^*_k
is the data, at time t_k
.
The prediction mean is therefore the expectation of this distribution
E[X(t_k) \vert Y(t_1)=y^*_1,\dots,Y(t_{k-1})=y^*_{k-1}].
See Also
More on sequential Monte Carlo methods:
bsmc2()
,
cond_logLik()
,
eff_sample_size()
,
filter_mean()
,
filter_traj()
,
kalman
,
mif2()
,
pfilter()
,
pmcmc()
,
pred_var()
,
saved_states()
,
wpfilter()
Other extraction methods:
coef()
,
cond_logLik()
,
covmat()
,
eff_sample_size()
,
filter_mean()
,
filter_traj()
,
forecast()
,
logLik
,
obs()
,
pred_var()
,
saved_states()
,
spy()
,
states()
,
summary()
,
time()
,
timezero()
,
traces()