ea_uniGaussian_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 tempered Gaussian distribution

Description

Simulate Langevin diffusion using the Exact Algorithm where pi = tempered Gaussian distribution

Usage

ea_uniGaussian_DL_PT(
  x0,
  y,
  s,
  t,
  mean,
  sd,
  beta,
  precondition,
  diffusion_estimator = "Poisson",
  beta_NB = 10,
  gamma_NB_n_points = 2,
  logarithm
)

Arguments

x0	start value
y	end value
s	start time
t	end time
mean	mean value
sd	standard deviation value
beta	real value
precondition	precondition value (i.e. the covariance for the Langevin diffusion)
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)
logarithm	logical value to determine if log probability is returned (TRUE) or not (FALSE)

Value

acceptance probability of simulating Langevin diffusion with pi = tempered Gaussian distribution

Examples

mu <- 0.423
sd <- 3.231
beta <- 0.8693
precondition <- 1.243
# Poisson estimator
ea_uniGaussian_DL_PT(x0 = 0,
                     y = 10,
                     s = 0,
                     t = 1,
                     mean = mu,
                     sd = sd,
                     beta = beta,
                     precondition = precondition,
                     logarithm = TRUE)
# NB estimator
ea_uniGaussian_DL_PT(x0 = 0,
                     y = 10,
                     s = 0,
                     t = 1,
                     mean = mu,
                     sd = sd,
                     beta = beta,
                     precondition = precondition,
                     logarithm = TRUE)

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