SGLD: Stochastic MCMC using Stochastic gradient Langevin dynamics

In feng-li/movingknots: Efficient Bayesian multivariate surface regression

Description

Fast version using stochastic gradient langevin dynamics.

Usage

SGLD(
  param.cur,
  logpost.fun.name,
  Params,
  Y,
  x0,
  callParam,
  splineArgs,
  priorArgs,
  algArgs,
  Params_Transform
)

Arguments

param.cur	NA
logpost.fun.name	MA
Params	NA
Y	NA
x0	NA
callParam	NA
splineArgs	NA
priorArgs	NA
algArgs	NA
Params_Transform	NA
Params_Transfor	NA

Value

NA

Author(s)

Feng Li, Central University of Finance and Economics.

References

Ma, Chen, & Fox 2015 A Complete Recipe for Stochastic Gradient MCMC.

feng-li/movingknots documentation built on March 30, 2021, 11:58 a.m.