Gibbs sampler for linear regression

gibbs_lm_R(y, X, steps)

Arguments

y	a vector of the response variable
X	a matrix whose columns are the explanatory variables
steps	number of iterations to run Gibbs sampler for

Value

a list of posterior samples for regression coefficients

Details

Assumes Normal likelihood and joint prior proportional to 1 / sigma2

Examples

if (FALSE) {
set.seed(1234)
n = 100
sigma = 40
beta = c(150, 5, 10)
x = seq(-4, 10, length = n)
y = beta[1] + beta[2] * x + beta[3] * x ^ 2 + rnorm(n, 0, sigma)
X = cbind(x, x ^ 2)
mcmc = gibbs_lm_R(y, X, 10000)
}