prior.gaussian: Use an isotropic Gaussian prior

In enweg/BFluxR: Implementation of Bayesian Neural Networks

Use an isotropic Gaussian prior

Description

Use a Multivariate Gaussian prior for all network parameters. Covariance matrix is set to be equal 'sigma * I' with 'I' being the identity matrix. Mean is zero.

Usage

prior.gaussian(chain, sigma)

Arguments

chain	Chain obtained using Chain
sigma	Standard deviation of Gaussian prior

Value

a list containing the following

• 'juliavar' the julia variable used to store the prior

• 'juliacode' the julia code

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)
  sampler <- sampler.SGLD()
  ch <- mcmc(bnn, 10, 1000, sampler)

## End(Not run)

enweg/BFluxR documentation built on Jan. 27, 2024, 6:43 p.m.