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