We consider a problem of inference for the output of a computationally expensive computer
model. We suppose that the model is to be used in a context where the values of one or …

Publisher Summary Statistical learning theory is regarded as one of the most beautifully
developed branches of artificial intelligence. It provides the theoretical basis for many of …

There has been an increasing interest in using expected value of information (EVI) theory in
medical decision making, to identify the need for further research to reduce uncertainty in …

Readers of this book will need no further convincing of the importance of Markov chain
Monte Carlo (MCMC) in numerical calculations for highly structured stochastic systems, and …

In geostatistics it is common practice to assume that the underlying spatial process is
stationary and isotropic, ie the spatial distribution is unchanged when the origin of the index …

We test a fully non-linear method to solve Bayesian seismic tomographic problems using
data consisting of observed traveltimes of first-arriving waves. Rather than using Monte …

This book presents Bayes' theorem, the estimation of unknown parameters, the
determination of confidence regions and the derivation of tests of hypotheses for the …

Behavioural studies have shown that while humans may be the best decision makers on the
planet, we are not quite as good as we think we are. We are regularly subject to biases …

Introduction literature and in the popular scientific press. 318 Pharmaceutical companies are
beginning to express an interest, possibly helped by the recent international regulatory …

The sparse linear model has seen many successful applications in Statistics, Machine
Learning, and Computational Biology, such as identification of gene regulatory networks …