The Bayesian Treatment of Auxiliary Hypotheses: Reply to Fitelson and Waterman

Bayesian Confirmation and Auxiliary Hypotheses Revisited: A Reply to Strevens

Michael Strevens [2001] has proposed an interesting and novel Bayesian analysis of the Quine-Duhem (Q–D) problem (i.e., the problem of auxiliary hypotheses). Strevens’s analysis involves the use of a simplifying idealization concerning the original Q–D problem. We will show that this idealization is far stronger than it might appear. Indeed, we argue that Strevens’s idealization oversimplifies ...

Why Likelihood

The Likelihood Principle has been defended on Bayesian grounds, on the grounds that it coincides with and systematizes intuitive judgments about example problems, and by appeal to the fact that it generalizes what is true when hypotheses have deductive consequences about observations. Here we divide the Principle into two parts -one qualitative, the other quantitative -and evaluate each in the ...

Bayesian Sample Size Determination for Joint Modeling of Longitudinal Measurements and Survival Data

A longitudinal study refers to collection of a response variable and possibly some explanatory variables at multiple follow-up times. In many clinical studies with longitudinal measurements, the response variable, for each patient is collected as long as an event of interest, which considered as clinical end point, occurs. Joint modeling of continuous longitudinal measurements and survival time...

Reply to the Comments on "Chemistry of Pyrolysis and Kinetic Studies of Shea Nut (Vitellaria Paradoxa) Shells Activated Carbon for Textile Wastewater Treatment"

Belief in Belief Functions: An Examination of Shafer's Canonical Examples

In the canonical examples underlying Shafer-Dempster theory, beliefs over the hypotheses of interest are derived from a probability model for a set of auxiliary hypotheses. Beliefs are derived via a compatibility relation connecting the auxiliary hypotheses to subsets of the primary hypotheses. A belief function differs from a Bayesian probability model in that one does not condition on those p...