prior_ensemble_model: Generate samples of parameters from prior distribution

Description

Methods to generates samples of the parameters from the prior distribution of the ensemble model.

Usage

prior_ensemble_model(
  priors,
  M = 1,
  MM = NULL,
  full_sample = TRUE,
  control = list(adapt_delta = 0.95),
  ...
)

Value

A list containing two items named samples and point_estimate. If full_sample==TRUE, samples is a stanfit and point_estimate is a NULL object, else samples is a NULL and point_estimate is a list object. It is possible to generate a point estimate for the prior if the individual short-term discrepancy prior follows a hierarchical parameterisation.

Arguments

priors: An EnsemblePrior object specifying the prior distributions for the ensemble.
M: A numeric that represents the number of simulators. The default is 1.
MM: A numeric that represents the number of drivers. The default is NULL.
full_sample: A logical that runs a full sampling of the prior density of the ensemble model if TRUE. If FALSE, returns the point estimate which maximises the prior density of the ensemble model.
control: If creating a full sample, this is a named list of parameters to control Stan's sampling behaviour. See the documentation of the stan() function in the rstan package for details. The default value is list(adapt_delta = 0.95). If optimizing, this value is ignored.
...: Additional arguments passed to the function rstan::sampling or rstan::optimizing.

References

Stan Development Team (2020). RStan: the R interface to Stan. R package version 2.21.2. https://mc-stan.org

Examples

Run this code

# \donttest{
priors <- EnsemblePrior(4)
prior_density <- prior_ensemble_model(priors, M = 4)
# }