The ensemble Kalman filter and ensemble adjustment Kalman filter.
# S4 method for pomp
enkf(object, params, Np, h, R,
verbose = getOption("verbose"), …)
# S4 method for pomp
eakf(object, params, Np, C, R,
verbose = getOption("verbose"), …)
# S4 method for kalmand.pomp
logLik(object, …)
# S4 method for kalmand.pomp
cond.logLik(object, …)
# S4 method for kalmand.pomp
pred.mean(object, pars, …)
# S4 method for kalmand.pomp
filter.mean(object, pars, …)An object of class pomp or inheriting class pomp.
optional named numeric vector containing the parameters at which the filtering should be performed.
By default, params = coef(object).
the number of particles to use.
logical; if TRUE, progress information is reported.
function returning the expected value of the observation given the state.
matrix converting state vector into expected value of the observation.
matrix; variance of the measurement noise.
Names of variables.
additional arguments (currently ignored).
An object of class kalmand.pomp.
This class inherits from class pomp.
Extracts the estimated log likelihood.
Extracts the estimated conditional log likelihood $$\ell_t(\theta) = \mathrm{Prob}[y_t \vert y_1, \dots, y_{t-1}],$$ where \(y_t\) are the data, at time \(t\).
Extract the mean of the approximate prediction distribution. This prediction distribution is that of $$X_t \vert y_1,\dots,y_{t-1},$$ where \(X_t\), \(y_t\) are the state vector and data, respectively, at time \(t\).
Extract the mean of the filtering distribution, which is that of $$X_t \vert y_1,\dots,y_t,$$ where \(X_t\), \(y_t\) are the state vector and data, respectively, at time \(t\).
Evensen, G. (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics Journal of Geophysical Research: Oceans 99:10143--10162
Evensen, G. (2009) Data assimilation: the ensemble Kalman filter Springer-Verlag.
Anderson, J. L. (2001) An Ensemble Adjustment Kalman Filter for Data Assimilation Monthly Weather Review 129:2884--2903
pomp, pfilter,
and the tutorials on the package website.