imis: Incremental Mixture Importance Sampling (IMIS)

In mrc-ide/eppasm: Age-structured EPP Model for HIV Epidemic Estimates

Incremental Mixture Importance Sampling (IMIS)

Description

Implements IMIS algorithm with optional optimization step (Raftery and Bao 2010).

Usage

imis(
  B0,
  B,
  B_re,
  number_k,
  opt_k = NULL,
  fp,
  likdat,
  prior = eppasm::prior,
  likelihood = eppasm::likelihood,
  sample_prior = eppasm::sample.prior,
  dsamp = eppasm::dsamp,
  save_all = FALSE
)

Arguments

B0	number of initial samples to draw
B	number of samples at each IMIS iteration
B_re	number of resamples
number_k	maximum number of iterations
opt_k	vector of iterations at which to use optimization step to identify new mixture component
fp	fixed model parameters
likdat	likeihood data
prior	function to calculate prior density for matrix of parameter inputs
likelihood	function to calculate likelihood for matrix of parameter inputs
sample_prior	function to draw an initial sample of parameter inputs
dsamp	function to calculate density for initial sampling distribution (may be equal to prior)
save_all	logical whether to save all sampled parameters

Value

list with items resample, stat, and center

mrc-ide/eppasm documentation built on March 25, 2024, 9:19 p.m.