We develop a representation of a decision maker's uncertainty based on e-variables. Like
the Bayesian posterior, this e-posterior allows for making predictions against arbitrary loss …

In this course we perform an introduction to Bayesian methods, we show some basic
definitions and properties of the bayesian approach. We have taken the content from the …

Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data
Analysis: An Introduction for Scientists and Statisticians presents statistical tools to address …

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

Ronald Fisher advocated testing using p-values, Harold Jeffreys proposed use of objective
posterior probabilities of hypotheses and Jerzy Neyman recommended testing with fixed …

We model the error distribution in the standard linear model as a mixture of absolutely
continuous Polya trees constrained to have median 0. By considering a mixture, we smooth …

Explanation is at the center of scientific research, and explanation almost always involves
the discovery of causal relations among factors, conditions, or events. This is true in the …

Mixtures of Polya tree models provide a flexible alternative when a parametric model may
only hold approximately. I provide computational strategies for obtaining full semiparametric …

We present a method for comparing semiparametric Bayesian models, constructed under
the Dirichlet process mixture (DPM) framework, with alternative semiparameteric or …

This chapter provides a brief review and motivation for the use of nonparametric Bayes
methods in biostatistical applications. Clearly, the nonparametric Bayes biostatistical …