|ess:Real||Effective sample size.|
|lsum:Real||Logarithm of sum of weights.|
|lnormalize:Real||Log normalizing constant.|
|npropagations:Integer||Number of propagations. This is not the same as the number of particles; the number of propagations performed may, according to the filter type, differ from the number of particles, such as for alive and rejection control particle filters.|
|raccept:Real||Accept rate of moves.|
|nsteps:Integer?||Number of steps. If this has no value, the model will be required to suggest an appropriate value.|
|nforecasts:Integer||Number of additional forecast steps per step.|
|nparticles:Integer||Number of particles.|
|trigger:Real||Threshold for resampling. Resampling is performed whenever the effective sample size, as a proportion of
|delayed:Boolean||Should delayed sampling be used?|
|particle||Create a particle of the type required for this filter.|
|filter||Filter first step.|
|filter||Filter one step.|
|reduce||Compute reductions, such as effective sample size and normalizing constant estimate.|
|write||Write only the current state to a buffer.|
Member Function Details
Filter first step.
Filter one step.
- t: The step number, beginning at 1.
- archetype: Archetype. This is an instance of the appropriate model class that may have one more random variables fixed to known values, representing the inference problem (or target distribution).
function particle(archetype:Model) -> Particle
Create a particle of the type required for this filter.
Compute reductions, such as effective sample size and normalizing constant estimate.
function size() -> Integer
Size. This is the number of steps of
filter(Model, Integer) to be
performed after the initial call to
filter(Model). Note that
initialize() must be called before
function write(buffer:Buffer, t:Integer)
Write only the current state to a buffer.