filter_mean: Filtering mean

In pomp: Statistical Inference for Partially Observed Markov Processes

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(t_k) \vert Y(t_1)=y^*_1,\dots,Y(t_k)=y^*_k,

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

The filtering mean is therefore the expectation of this distribution

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(), timezero(), time(), traces()

pomp documentation built on Aug. 8, 2023, 1:08 a.m.