progressive_fusion_uniGaussian: (Progressive) D&C Monte Carlo Fusion (rejection sampling)

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

(Progressive) D&C Monte Carlo Fusion (rejection sampling)

Description

(Progressive) D&C Monte Carlo Fusion with univariate Gaussian target

Usage

progressive_fusion_uniGaussian(
  N_schedule,
  time_schedule,
  base_samples,
  mean,
  sd,
  start_beta,
  precondition = TRUE,
  seed = NULL,
  n_cores = parallel::detectCores()
)

Arguments

N_schedule	vector of length (L-1), where N_schedule[l] is the number of samples per node at level l
time_schedule	vector of length(L-1), where time_schedule[l] is the time chosen for Fusion at level l
base_samples	list of length (1/start_beta), where base_samples[[c]] contains the samples for the c-th node in the level
mean	mean value
sd	standard deviation value
start_beta	beta for the base level
precondition	either a logical value to determine if preconditioning values are used (TRUE - and is set to be the variance of the sub-posterior samples) or not (FALSE - and is set to be 1 for all sub-posteriors), or a list of length (1/start_beta) where precondition[[c]] is the preconditioning value for sub-posterior c. Default is TRUE
seed	seed number - default is NULL, meaning there is no seed
n_cores	number of cores to use

Value

A list with components:

samples

list of length (L-1), where samples[[l]][[i]] are the samples for level l, node i

time

list of length (L-1), where time[[l]][[i]] is the run time for level l, node i

rho_acc

list of length (L-1), where rho_acc[[l]][i] is the acceptance rate for first fusion step for level l, node i

Q_acc

list of length (L-1), where Q_acc[[l]][i] is the acceptance rate for second fusion step for level l, node i

rhoQ_acc

list of length (L-1), where rhoQ_acc[[l]][i] is the overall acceptance rate for fusion for level l, node i

diffusion_times

vector of length (L-1), where diffusion_times[l] are the times for fusion in level l (= time_schedule)

precondition_values

preconditioning values used in the algorithm for each node

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