parallel_fusion_SMC_multiGaussian: Generalised Monte Carlo Fusion [parallel]

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

Generalised Monte Carlo Fusion [parallel]

Description

Generalised Monte Carlo Fusion with multivariate Gaussian target

Usage

parallel_fusion_SMC_multiGaussian(
  particles_to_fuse,
  N,
  m,
  time,
  dim,
  mu,
  Sigma,
  betas,
  precondition_matrices,
  resampling_method = "multi",
  ESS_threshold = 0.5,
  diffusion_estimator = "Poisson",
  beta_NB = 10,
  gamma_NB_n_points = 2,
  seed = NULL,
  n_cores = parallel::detectCores()
)

Arguments

particles_to_fuse	list of length m, where particles_to_fuse[[c]] contains the particles for the c-th sub-posterior (a list of particles to fuse can be initialised by initialise_particle_sets() function)
N	number of samples
m	number of sub-posteriors to combine
time	time T for fusion algorithm
dim	dimension
mu	vector of length dim for mean
Sigma	dim x dim covariance matrix
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
resampling_method	method to be used in resampling, default is multinomial resampling ('multi'). Other choices are stratified resampling ('strat'), systematic resampling ('system'), residual resampling ('resid')
ESS_threshold	number between 0 and 1 defining the proportion of the number of samples that ESS needs to be lower than for resampling (i.e. resampling is carried out only when ESS < N*ESS_threshold)
diffusion_estimator	choice of unbiased estimator for the Exact Algorithm between "Poisson" (default) for Poisson estimator and "NB" for Negative Binomial estimator
beta_NB	beta parameter for Negative Binomial estimator (default 10)
gamma_NB_n_points	number of points used in the trapezoidal estimation of the integral found in the mean of the negative binomial estimator (default is 2)
seed	seed number - default is NULL, meaning there is no seed
n_cores	number of cores to use

Value

A list with components:

particles

particles returned from fusion sampler

proposed_samples

proposal samples from fusion sampler

time

run-time of fusion sampler

ESS

effective sample size of the particles after each step

CESS

conditional effective sample size of the particles after each step

resampled

boolean value to indicate if particles were resampled after each time step

precondition_matrices

list of length dim 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.