BayesFluxR (version 0.1.3)

sampler.SGNHTS: Stochastic Gradient Nose-Hoover Thermostat as proposed in

Description

Proposed in Leimkuhler, B., & Shang, X. (2016). Adaptive thermostats for noisy gradient systems. SIAM Journal on Scientific Computing, 38(2), A712-A736.

Usage

sampler.SGNHTS(
  l,
  sigmaA = 1,
  xi = 1,
  mu = 1,
  madapter = madapter.FixedMassMatrix()
)

Value

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

Arguments

l

Stepsize

sigmaA

Diffusion factor

xi

Thermostat

mu

Free parameter of thermostat

madapter

Mass Adapter; Not used in original paper and thus has no theoretical backing

Details

This is similar to SGNHT as proposed in Ding, N., Fang, Y., Babbush, R., Chen, C., Skeel, R. D., & Neven, H. (2014). Bayesian sampling using stochastic gradient thermostats. Advances in neural information processing systems, 27.

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)
  sampler <- sampler.SGNHTS(1e-3)
  ch <- mcmc(bnn, 10, 1000, sampler)
}