ea_exp_4_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 with pi = exp(-(beta*(x-mean)^4)/2)

Description

Evaluate probability of Langevin diffusion using the Exact Algorithm with pi = exp(-(beta*(x-mean)^4)/2) using Poisson thinning

Usage

ea_exp_4_DL_PT(
  x0,
  y,
  s,
  t,
  mean,
  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
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)
K	lower bound of the phi function

Value

acceptance probability of simulating Langevin diffusion with pi = exp(-(beta*(x-mean)^4)/2)

Examples

mu <- 0.435
beta <- 0.583
precondition <- 1.243
# Poisson estimator
ea_exp_4_DL_PT(x0 = 0,
               y = 0.23,
               s = 0,
               t = 1,
               mean = mu,
               beta = beta,
               precondition = precondition,
               logarithm = TRUE)
# NB estimator
ea_exp_4_DL_PT(x0 = 0,
               y = 0.23,
               s = 0,
               t = 1,
               mean = mu,
               beta = beta,
               precondition = precondition,
               diffusion_estimator = 'NB',
               logarithm = TRUE)

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