Bayesian variable and link determination for generalised linear models

In this paper, we describe full Bayesian inference for generalised linear models where uncertainty exists about the structure of the linear predictor, the linear parameters and the link function. Choice of suitable prior distributions is discussed in detail and we propose an efficient reversible jump Markov chain Monte-Carlo algorithm for calculating posterior summaries. We illustrate our method with two data examples.