We give a short review on the Bayesian approach for neural network learning and
demonstrate the advantages of the approach in three real applications. We discuss the …

This chapter reviews the recent developments in Markov chain Monte Carlo simulation
methods. These methods, which are concerned with the simulation of high dimensional …

This book provides a self-contained and up-to-date treatment of the Monte Carlo method
and develops a common framework under which various Monte Carlo techniques can be" …

This purpose of this introductory paper is threefold. First, it introduces the Monte Carlo
method with emphasis on probabilistic machine learning. Second, it reviews the main …

Classification is one of the most active research and application areas of neural networks.
The literature is vast and growing. This paper summarizes some of the most important …

The decade prior to publication has seen an explosive growth in com-tational speed and
memory and a rapid enrichment in our understa-ing of arti? cial neural networks. These two …

Kendall's Advanced Theory of Statistics and Kendall's Library of Statistics The development
of modern statistical theory in the past fifty years is reflected in the history of the late Sir …

We review the current state of nonparametric Bayesian inference. The discussion follows a
list of important statistical inference problems, including density estimation, regression …

Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the
efficiency of learning on a novel task. This approach encounters difficulty when transfer is …

Support vector machines (SVMs) appeared in the early nineties as optimal margin classifiers
in the context of Vapnik's statistical learning theory. Since then SVMs have been …