# filter_mean: Filtering mean In pomp: Statistical Inference for Partially Observed Markov Processes

 filter_mean 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(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].`

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()`