The mean of the filtering distribution
# S4 method for kalmand_pomp
filter.mean(object, vars, ...)# S4 method for pfilterd_pomp
filter.mean(object, vars, ...)
result of a filtering computation
optional character; names of variables
ignored
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 particle-filter based methods in pomp:
bsmc2(),
cond.logLik(),
eff.sample.size(),
filter.traj(),
kalman,
mif2(),
pfilter(),
pmcmc(),
pred.mean(),
pred.var(),
saved.states(),
wpfilter()