Posterior consistency in linear models under shrinkage priors

We investigate the asymptotic behaviour of posterior distributions of regression coefficients in highdimensional linear models as the number of dimensions grows with the number of observations. We show that the posterior distribution concentrates in neighbourhoods of the true parameter under simple sufficient conditions. These conditions hold under popular shrinkage priors given some sparsity assumptions.