Q_IS_BRR: Q Importance Sampling Step

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

Q Importance Sampling Step

Description

Q Importance Sampling weighting for Bayesian robust regression

Usage

Q_IS_BRR(
  particle_set,
  m,
  time,
  dim,
  data_split,
  nu,
  sigma,
  prior_means,
  prior_variances,
  C,
  proposal_cov,
  precondition_matrices,
  inv_precondition_matrices,
  diffusion_estimator,
  beta_NB = 10,
  gamma_NB_n_points = 2,
  seed = NULL,
  n_cores = parallel::detectCores(),
  cl = NULL,
  level = 1,
  node = 1,
  print_progress_iters = 1000
)

Arguments

particle_set	particles set prior to Q importance sampling step
m	number of sub-posteriors to combine
time	time T for fusion algorithm
dim	dimension of the predictors (= p+1)
data_split	list of length m where each item is a list of length 4 where for c=1,...,m, data_split[[c]]$y is the vector for y responses and data_split[[c]]$X is the design matrix for the covariates for sub-posterior c
nu	degrees of freedom in t-distribution
sigma	scale parameter in t-distribution
prior_means	prior for means of predictors
prior_variances	prior for variances of predictors
C	overall number of sub-posteriors
proposal_cov	proposal covariance of Gaussian distribution for Fusion
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
cl	an object of class "cluster" for parallel computation in R. If none is passed, then one is created and used within this function
level	indicates which level this is for the hierarchy (default 1)
node	indicates which node this is for the hierarchy (default 1)
print_progress_iters	number of iterations between each progress update (default is 1000). If NULL, progress will only be updated when importance sampling is finished

Value

An updated particle set

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