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 …

This article deals with specifications of informative prior distributions for generalized linear
models. Our emphasis is on specifying distributions for selected points on the regression …

Fellow users of probability, statistics, and computer “learning” algorithms, physics and social
science modelers, big data wranglers, philosophers of science, epistemologists, and other …

This article describes an approach to incorporate expert opinion on observable quantities
through the use of a loss function which updates a prior belief as opposed to specifying …

Background In decision modeling with time-to-event data, there are a variety of parametric
models that can be used to extrapolate the survival function. Each model implies a different …

Praise for Bayesian Thinking in Biostatistics:" This thoroughly modern Bayesian book… is
a'must have'as a textbook or a reference volume. Rosner, Laud and Johnson make the case …

In this paper, using Marshall-Olkin transformation, a new class of Extended Power Log-
normal distribution which includes the Power Log-normal and Log-normal distributions as …

Standard methods for analysing survival data with covariates rely on asymptotic inferences.
Bayesian methods can be performed using simple computations and are applicable for any …

This thesis investigates methods used to predict long-term survival of observations (typically
survival times) beyond the time at which data follow-up is available. Current practice is to …

Statistical models are probability models and physical models are causal or deterministic or
mixed causal-deterministic-probability models applied to observable propositions. It is …