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Ensemble Kalman Filter Prediction.
tfp.experimental.sequential.ensemble_kalman_filter_predict(
state, transition_fn, seed=None, inflate_fn=None, name=None
)
The Ensemble Kalman Filter is a Monte Carlo version of the traditional Kalman Filter.
This method is the 'prediction' equation associated with the Ensemble
Kalman Filter. This takes in an optional inflate_fn to perform covariance
inflation on the ensemble [2].
Args | |
|---|---|
state
|
Instance of EnsembleKalmanFilterState.
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transition_fn
|
callable returning a (joint) distribution over the next
latent state, and any information in the extra state.
Each component should be an instance of
MultivariateNormalLinearOperator.
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seed
|
Python int seed for random ops.
|
inflate_fn
|
Function that takes in the particles and returns a
new set of particles. Used for inflating the covariance of points.
Note this function should try to preserve the sample mean of the
particles, and scale up the sample covariance.
|
name
|
Python str name for ops created by this method.
Default value: None (i.e., 'ensemble_kalman_filter_predict').
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Returns | |
|---|---|
next_state
|
EnsembleKalmanFilterState representing particles after
applying transition_fn.
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References
[1] Geir Evensen. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 1994.
[2] Jeffrey L. Anderson and Stephen L. Anderson. A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts. Monthly Weather Review, 1999.