Q_IS_biGaussian: Q Importance Sampling Step

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

Q Importance Sampling Step

Description

Q Importance Sampling weighting for bivariate Gaussian distributions

Usage

Q_IS_biGaussian(
  particle_set,
  m,
  time,
  mean_vec,
  sd_vec,
  corr,
  betas,
  precondition_matrices,
  inv_precondition_matrices,
  diffusion_estimator = "Poisson",
  beta_NB = 10,
  gamma_NB_n_points = 2,
  seed = NULL,
  n_cores = parallel::detectCores()
)

Arguments

particle_set	particles set prior to Q importance sampling step
m	number of sub-posteriors to combine
time	time T for fusion algorithm
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
inv_precondition_matrices	list of length m, where inv_precondition_matrices[[c]] is the inverse precondition matrix for sub-posterior c
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

An updated particle set

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