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.