sadapter.DualAverage {BayesFluxR}

Use Dual Averaging like in STAN to tune stepsize

Description

Use Dual Averaging like in STAN to tune stepsize

Usage

sadapter.DualAverage(
  adapt_steps,
  initial_stepsize = 1,
  target_accept = 0.65,
  gamma = 0.05,
  t0 = 10,
  kappa = 0.75
)

Arguments

adapt_steps

number of adaptation steps

initial_stepsize

initial stepsize

target_accept

target acceptance ratio

gamma

See STAN manual NUTS paper

t0

See STAN manual or NUTS paper

kappa

See STAN manual or NUTS paper

Value

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)
  sadapter <- sadapter.DualAverage(100)
  sampler <- sampler.GGMC(sadapter = sadapter)
  ch <- mcmc(bnn, 10, 1000, sampler)

## End(Not run)