BayesFluxR (version 0.1.3)

sampler.HMC: Standard Hamiltonian Monte Carlo (Hybrid Monte Carlo).

Description

Allows for the use of stochastic gradients, but the validity of doing so is not clear.

Usage

sampler.HMC(
  l,
  path_len,
  sadapter = sadapter.DualAverage(1000),
  madapter = madapter.FixedMassMatrix()
)

Value

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

Arguments

l

stepsize

path_len

number of leapfrog steps

sadapter

Stepsize adapter

madapter

Mass adapter

Details

This is motivated by parts of the discussion in Neal, R. M. (1996). Bayesian Learning for Neural Networks (Vol. 118). Springer New York. https://doi.org/10.1007/978-1-4612-0745-0

Examples

if (FALSE) {
  ## 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.HMC(1e-3, 3, sadapter = sadapter)
  ch <- mcmc(bnn, 10, 1000, sampler)
}