NUTS: The function to implement The No-U-Turn Sampler (NUTS)

In yanbowisc/SIMP: Bayesian Simultaneous Partial Envelope Model

The function to implement The No-U-Turn Sampler (NUTS)

Description

The function to implement The No-U-Turn Sampler (NUTS)

Usage

NUTS(
  A,
  j,
  iter,
  lpd_func,
  grad_lpd_func,
  M_adapt,
  M_diag,
  delta = 0.6,
  max_treedepth = 10,
  eps = 1,
  eps_bar,
  H,
  mu,
  verbose = TRUE,
  Delta_max,
  ...
)

Arguments

A	Parameter A or B.
j	The index for which column of A or B to be updated.
iter	The iteration index.
lpd_func	The log full conditional posterior density of A or B.
grad_lpd_func	The function to calculate the gradients of the log full conditional posterior density of A or B.
M_adapt	The M_adapt parameter used in the Algorithm 6 of NUTS.
M_diag	Covariance matrix for our matrix parameter A or B.
delta	The delta parameter used in the Algorithm 6 of NUTS.
max_treedepth	The maximal tree depth.
eps	The initial value for the stepsize tuning for find_reasonable_epsilon().
eps_bar	The eps_bar parameter used in the Algorithm 6 of NUTS.
H	The H_m in the Algorithm 6 of NUTS.
mu	The mu in the Algorithm 6 of NUTS.
verbose	Whether to show the progress messages for find_reasonable_epsilon().
Delta_max	The Delta_max parameter used in the Algorithm 6 of NUTS.
...	Other parameters that may be useful.

yanbowisc/SIMP documentation built on Oct. 30, 2022, 1:33 a.m.