  We propose discrete determinantal point processes (DPPs) for priors on the
model parameter in Bayesian variable selection. By our variable selection
method, collinear predictors are less likely to be selected simultaneously
because of the repulsion property of discrete DPPs. Three types of DPP priors
are proposed. We show the efficiency of the proposed priors through numerical
experiments and applications to collinear datasets.
