In the era of big data, analysts usually explore various statistical models or machine-learning
methods for observed data to facilitate scientific discoveries or gain predictive power …

A substantial school in the philosophy of science identifies Bayesian inference with inductive
inference and even rationality as such, and seems to be strengthened by the rise and …

It is shown that training of generative adversarial network (GAN) may not have good
generalization properties; eg, training may appear successful but the trained distribution …

Function and shape data analysis are old topics in statistics, studied off and on over the last
several decades. However, the early years of the new millennium saw a renewed focus and …

We propose a framework for general Bayesian inference. We argue that a valid update of a
prior belief distribution to a posterior can be made for parameters which are connected to …

ABSTRACT A key challenge for modern Bayesian statistics is how to perform scalable
inference of posterior distributions. To address this challenge, variational Bayes (VB) …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

This package includes both Mathematical Statistics: Basic Ideas and Selected Topics,
Volume I, Second Edition, as well as Mathematical Statistics: Basic Ideas and Selected …

Bayesian Statistics: Page 1 Bayesian Statistics: An Introduction PETER M. LEE Formerly Provost
of Wentworth College, University of York, England Fourth Edition John Wiley & Sons, Ltd Page …

The growth of biostatistics has been phenomenal in recent years and has been marked by
considerable technical innovation in both methodology and computational practicality. One …