madapter.DiagCov {BayesFluxR}

Use the diagonal of sample covariance matrix as inverse mass matrix.

Description

Use the diagonal of sample covariance matrix as inverse mass matrix.

Usage

madapter.DiagCov(adapt_steps, windowlength, kappa = 0.5, epsilon = 1e-06)

Arguments

adapt_steps

Number of adaptation steps

windowlength

Lookback window length for calculation of covariance

kappa

How much to shrink towards the identity

epsilon

Small value to add to diagonal so as to avoid numerical non-pos-def problem

Value

list containing 'juliavar' and '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)
  madapter <- madapter.DiagCov(100, 10)
  sampler <- sampler.GGMC(madapter = madapter)
  ch <- mcmc(bnn, 10, 1000, sampler)

## End(Not run)