filter_mean {pomp}R Documentation

Filtering mean

Description

The mean of the filtering distribution

Usage

## S4 method for signature 'kalmand_pomp'
filter_mean(object, vars, ..., format = c("array", "data.frame"))

## S4 method for signature 'pfilterd_pomp'
filter_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 filtering distribution is that of

X(tk)Y(t1)=y1,,Y(tk)=yk,X(t_k) \vert Y(t_1)=y^*_1,\dots,Y(t_k)=y^*_k,

where X(tk)X(t_k), Y(tk)Y(t_k) are the latent state and observable processes, respectively, and yty^*_t is the data, at time tkt_k.

The filtering mean is therefore the expectation of this distribution

E[X(tk)Y(t1)=y1,,Y(tk)=yk].E[X(t_k) \vert Y(t_1)=y^*_1,\dots,Y(t_k)=y^*_k].

See Also

More on sequential Monte Carlo methods: bsmc2(), cond_logLik(), eff_sample_size(), filter_traj(), kalman, mif2(), pfilter(), pmcmc(), pred_mean(), pred_var(), saved_states(), wpfilter()

Other extraction methods: coef(), cond_logLik(), covmat(), eff_sample_size(), filter_traj(), forecast(), logLik, obs(), pred_mean(), pred_var(), saved_states(), spy(), states(), summary(), time(), timezero(), traces()


[Package pomp version 5.11.0.0 Index]