In goldingn/greta: Simple and Scalable Statistical Modelling in R

Bayesian neural network

Bayesian neural network estimates an easy neural network with a normal prior on the edge weights. For clarity we use an architecture without a hidden layer, such that the weights actually correspond to coefficients in a linear regression model.

N <- 100
p <- 10

set.seed(23)  
X <- matrix(rnorm(N * p), N)
beta <- rnorm(10)
y <- X %*% beta + rnorm(N, sd = 0.1)

neural_network <- function(x)
{
  # this can be arbitrarily complex, e.g. multiple hidden layers
  x %*% weights
}

weights <- normal(0, 1, dim = c(p, 1))
sd <- inverse_gamma(1, 1)

distribution(y) <- normal(neural_network(X), sd)

goldingn/greta documentation built on May 24, 2021, 11 a.m.