ea_BLR_DL_PT: Diffusion probability for the Exact Algorithm for Langevin...

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

Diffusion probability for the Exact Algorithm for Langevin diffusion for Bayesian logistic regression

Description

Simulate Langevin diffusion using the Exact Algorithm where target is the posterior for a logistic regression model with Gaussian priors

Usage

ea_BLR_DL_PT(
  dim,
  x0,
  y,
  s,
  t,
  data,
  transformed_design_mat,
  prior_means,
  prior_variances,
  C,
  precondition_mat,
  transform_mats,
  cv_location = "hypercube_centre",
  diffusion_estimator,
  beta_NB = 10,
  gamma_NB_n_points = 2,
  local_bounds = TRUE,
  logarithm
)

Arguments

dim	dimension of the predictors (= p+1)
x0	start value (vector of length dim)
y	end value (vector of length dim)
s	start time
t	end time
data	list of length 4 where data[[c]]$y is the vector for y responses and data[[c]]$X is the design matrix for the covariates for sub-posterior c, data[[c]]$full_data_count is the unique rows of the full data set with their counts and data[[c]]$design_count is the unique rows of the design matrix and their counts
prior_means	prior for means of predictors
prior_variances	prior for variances of predictors
C	overall number of sub-posteriors
precondition_mat	precondition matrix
transform_mats	list of transformation matrices where transform_mats$to_Z is the transformation matrix to Z space and transform_mats$to_X is the transformation matrix to X space
cv_location	string to determine what the location of the control variate should be. Must be either 'mode' where the MLE estimator will be used or 'hypercube_centre' (default) to use the centre of the simulated hypercube
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)
local_bounds	logical value indicating if local bounds for the phi function are used (default is TRUE)
logarithm	logical value to determine if log probability is returned (TRUE) or not (FALSE)

Value

acceptance probability of simulating Langevin diffusion where target is the posterior for a logistic regression model with Gaussian priors

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