IMIS_: Incremental Mixture Importance Sampling (IMIS) - amended...
In W-Mohammed/calibrater: Calibration of Decision-Analytic Models

IMIS_

R Documentation

Incremental Mixture Importance Sampling (IMIS) - amended version

Description

Incremental Mixture Importance Sampling (IMIS) - amended version

Usage

IMIS_(
  B = 1000,
  B.re = 3000,
  number_k = 100,
  D = 0,
  sample.prior = .sample.prior_,
  priors = .priors_,
  prior = .prior_,
  likelihood = .likelihood_,
  .l_params_,
  .func_,
  .args_,
  .l_targets_,
  .transform_
)

Arguments

`B`	Sample size at each IMIS iteration
`B.re`	Desired posterior sample size
`number_k`	Maximum number of iterations in IMIS
`D`	use optimizer >= 1, do not use = 0
`sample.prior`	Sample from prior distribution
`priors`	Function that calculates prior densities (many sets of parameters at a time)
`prior`	Function that calculates prior densities (one set of parameters at a time)
`likelihood`	A function that calculates the likelihood
`.l_params_`	A list with parameters information
`.func_`	A function defining the model to be calibrated
`.args_`	A list of arguments passed to the model function.
`.l_targets_`	A list containing a vector of targets' names, a vector of targets' weights, a vector of targets' distributions, and a table for each target that contains the values (column name 'value') and standard errors (column name 'sd') of the corresponding target.
`.transform_`	Logical for whether to back-transform parameters to their original scale.