Description Usage Arguments Value Note
This function allows you to optimize the number of particles in your SMC. It displays how the variance and the mean of the likelihood estimator change with increasing number of particles. The idea is then to select an intermediate number of particles such that the estimator is stable, thus saving further computation burden in your PMCMC.
1 | calibrate_smc(ssm, n_parts, n_replicates, plot = TRUE, ...)
|
ssm |
a |
n_parts |
numeric vector containing the number of particles to test. |
n_replicates |
for each value of |
plot |
logical, if |
... |
other parameters to be passed to |
a list of 3 elements:
smc
a data_frame
containing one ssm
object per SMC run.
summary
a data_frame
containing the SSM summary of each SMC run (loglikelihood etc.).
plot
a list of 2 plots: boxplot
and mean_var
This function will always run an SMC with PSR approximation since it's the only situation where you will be interested in calibrating the number of particles.
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