sampler.GGMC {BayesFluxR}

Gradient Guided Monte Carlo

Description

Proposed in Garriga-Alonso, A., & Fortuin, V. (2021). Exact langevin dynamics with stochastic gradients. arXiv preprint arXiv:2102.01691.

Usage

sampler.GGMC(
  beta = 0.1,
  l = 1,
  sadapter = sadapter.DualAverage(1000),
  madapter = madapter.FixedMassMatrix(),
  steps = 3
)

Arguments

beta

See paper

l

stepsize

sadapter

Stepsize adapter; Not used in original paper

madapter

Mass adapter; Not used in ogirinal paper

steps

Number of steps before accept/reject

Value

a list with 'juliavar', 'juliacode' and all provided 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)
  sadapter <- sadapter.DualAverage(100)
  sampler <- sampler.GGMC(sadapter = sadapter)
  ch <- mcmc(bnn, 10, 1000, sampler)

## End(Not run)