  The present paper is about estimation and prediction in high-dimensional
additive models under a sparsity assumption ($p\gg n$ paradigm). A PAC-Bayesian
strategy is investigated, delivering oracle inequalities in probability. The
implementation is performed through recent outcomes in high-dimensional MCMC
algorithms, and the performance of our method is assessed on simulated data.
