Lp–quantiles generalise quantiles and expectiles to account for the whole distribution of the random variable of interest. In this paper, we introduce the Lp– quantile regression model, we propose a collapsed Gibbs–sampler algorithm to make Bayesian inference on the regression parameters. We also provide some theoretical results concerning the posterior distribution of the regression parameters