Approximate Bayesian computation methods

Abstract

Occasionally, Statistics and Computing is publishing Special Issues on topics of potential interests. The most recent published Special Issues were concerned with “Adaptive Methods in Bayesian Computation”, Guest Editor Paul Fearnhead, Volume 18 Issue 4 (2008), “Regularisation Methods in Classification and Regression”, Guest Editor Gerhard Tutz, Volume 20 Issue 2 (2009), and “Modeling of Computer Experiments for Uncertainty Propagation and Sensitivity Analysis”, Guest Editors Anestis Antoniadis and Alberto Pasanisi, Volume 22 Issue 3 (2012). Usually, those special issues are started with a “Call for papers” giving their purpose and specifying their desired subjects. This issue is proposing a Special Issue on Approximate Bayesian Computation (ABC) methods, which has not been conceived according to this scheme. Actually, ABC methodology is an emerging domain of computational statistics and there was no need to prepare a formal “Call for papers” for this special issue. The project of this special issue simply rises because, since early 2010, Statistics and Computing has received a lot of submissions on this new topic of computational statistics. And ABC methodology is typically a good material for Statistics and Computing. Roughly speaking each ten years, a new methodology of computational statistics appears and dominates the scene for a while, it was the bootstrap, Efron (1979), and the algorithms related to the EM algorithm of Dempster et al. (1977) in the eighties, Monte Carlo Markov Chains for Bayesian analysis in the nineties (Gelfand and Smith 1990), regularization methods derived from the Lasso (Tibshirani 1996)