parallel_fusion_biGaussian: Generalised Monte Carlo Fusion (rejection sampling)...

In rchan26/hierarchicalFusion: Divide-and-Conquer Monte Carlo Fusion

Generalised Monte Carlo Fusion (rejection sampling) [parallel]

Description

Generalised Monte Carlo Fusion with bivariate Gaussian target

Usage

parallel_fusion_biGaussian(
  N,
  m,
  time,
  samples_to_fuse,
  mean_vec,
  sd_vec,
  corr,
  betas,
  precondition_matrices,
  seed = NULL,
  n_cores = parallel::detectCores()
)

Arguments

N	number of samples
m	number of sub-posteriors to combine
time	time T for fusion algorithm
samples_to_fuse	list of length m, where samples_to_fuse[[c]] contains the samples for the c-th sub-posterior
mean_vec	vector of length 2 for mean
sd_vec	vector of length 2 for standard deviation
corr	correlation value between component 1 and component 2
betas	vector of length c, where betas[c] is the inverse temperature value for c-th posterior
precondition_matrices	list of length m, where precondition_matrices[[c]] is the precondition matrix for sub-posterior c
seed	seed number - default is NULL, meaning there is no seed
n_cores	number of cores to use

Value

A list with components:

samples

fusion samples

rho

acceptance rate for rho step

Q

acceptance rate for Q step

rhoQ

overall acceptance rate

time

run-time of fusion sampler

rho_iterations

number of iterations for rho step

Q_iterations

number of iterations for Q step

precondition_matrices

list of length 2 where precondition_matrices[[2]] are the pre-conditioning matrices that were used and precondition_matrices[[1]] are the combined precondition matrices

rchan26/hierarchicalFusion documentation built on Sept. 11, 2022, 10:30 p.m.