sampler.SGLD {BayesFluxR}

Stochastic Gradient Langevin Dynamics as proposed in Welling, M., & Teh, Y. W. (n.d.). Bayesian Learning via Stochastic Gradient Langevin Dynamics. 8.

Description

Stepsizes will be adapted according to

a(b+t)^{-\gamma}

Usage

sampler.SGLD(
  stepsize_a = 0.1,
  stepsize_b = 0,
  stepsize_gamma = 0.55,
  min_stepsize = -Inf
)

Arguments

stepsize_a

See eq. above

stepsize_b

See eq. above

stepsize_gamma

see eq. above

min_stepsize

Do not decrease stepsize beyond this

Value

a list with 'juliavar', 'juliacode', and all given arguments

Examples

## Not run: 
  ## Needs previous call to `BayesFluxR_setup` which is time
  ## consuming and requires Julia and BayesFlux.jl
  BayesFluxR_setup(installJulia=TRUE, seed=123)
  net <- Chain(Dense(5, 1))
  like <- likelihood.feedforward_normal(net, Gamma(2.0, 0.5))
  prior <- prior.gaussian(net, 0.5)
  init <- initialise.allsame(Normal(0, 0.5), like, prior)
  x <- matrix(rnorm(5*100), nrow = 5)
  y <- rnorm(100)
  bnn <- BNN(x, y, like, prior, init)
  sampler <- sampler.SGLD()
  ch <- mcmc(bnn, 10, 1000, sampler)

## End(Not run)