Bayesian inference

This book is a compilation of unpublished papers written by Jean-Marie Rolin (with several co-authors) on nonparametric bayesian estimation.

This textbook reconstructs the statistics curriculum from the perspective of posterior probability.

This textbook provides a concise introduction to optimal experimental design and efficiently prepares the reader for research in the area.

This graduate textbook on the statistical approach to data science describes the basic ideas, scientific principles and common techniques for the extraction of mathematical models from observed data.

This book presents a wide range of topics to address the needs of several groups of users of rapidly growing methods of generalized linear models.

This book is a rigorous but practical presentation of the Bayesian techniques of uncertainty quantification, with applications in R.

This book provides a self-contained introduction of mixed-effects models and small area estimation techniques.

This textbook reconstructs the statistics curriculum from the perspective of posterior probability.

This textbook provides a concise introduction to optimal experimental design and efficiently prepares the reader for research in the area.

This richly illustrated textbook covers modern statistical methods with applications in medicine, epidemiology and biology.

This book offers a comprehensive overview of statistical methodology for modelling and evaluating spatial variables useful in a variety of applications.

This book has been replaced by Bayesian Statistics for the Social Sciences, Second Edition, ISBN 978-1-4625-5354-9.