The problem of data reduction when no such sufficient statistic exists is addressed by Wolf and George through the introduction of maximally informative statistics. Several authors propose advances on Bayesian methods which may be specially suited to the analysis of experimental results. Motivated by the scientific need to replicate published experimental results, Bayarri and Mayoral show how graphical models can be used to simplify the analysis of hierarchical models. As a general method for prediction of future observations, Bernardo describes the construction of model-free objective predictive distributions. To address the problem posed by non-linear scale transformations, Farrow discusses an extension of Bayes linear networks.
Some contributions contain a Bayesian analysis of concrete scientific problems.
Motivated by problems in infrared spectroscopy, Fearn, Brown and Haque address the problem of probabilistic classification using a Bayesian form of logistic regression. Bayesian computation often requires sophisticated integration procedures. Toivonen, Mannila, Heikki, Salmenkivi and Laakso describe Bassist , a piece of computer software specially designed to implement the simulations required by the modern integration methods based on the use of Markov chain Monte Carlo techniques. We would like to thank Leticia de las Heras for her work in the preparation of this volume, and we are most grateful to the Spanish Real Academia de Ciencias for this opportunity to provide information on Bayesian methodology to the scientific community.