Prior Distributions for Objective Bayesian Analysis

Prior-free Inference for Objective Bayesian Analysis and Model Selection

A new approach to Bayesian inference, named the prior-free inference, is introduced for developing objective Bayesian analysis based on information-theoretic approach. This new approach is essentially a Bayesian method but it does not depend on a prior distribution for unknown parameters. Thus, this approach not only has the advantages of the Bayesian approach but also can avoid the difficulty,...

Bayesian prior probability distributions for internal dosimetry.

The problem of choosing a prior distribution for the Bayesian interpretation of measurements (specifically internal dosimetry measurements) is considered using a theoretical analysis and by examining historical tritium and plutonium urine bioassay data from Los Alamos. Two models for the prior probability distribution are proposed: (1) the log-normal distribution, when there is some additional ...

Normalized Power Prior Bayesian Analysis Normalized Power Prior Bayesian Analysis

Ranking Prior Likelihood Distributions for Bayesian Shape Localization Framework

In this paper, we formulate the shape localization problem in the Bayesian framework. In the learning stage, we propose the Constrained RankBoost approach to model the likelihood of local features associated with the key points of an object, like face, while preserve the prior ranking order between the ground truth position of a key point and its neighbors; in the inferring stage, a simple effi...

The Case for Objective Bayesian Analysis

Bayesian statistical practice makes extensive use of versions of objective Bayesian analysis. We discuss why this is so, and address some of the criticisms that have been raised concerning objective Bayesian analysis. The dangers of treating the issue too casually are also considered. In particular, we suggest that the statistical community should accept formal objective Bayesian techniques wit...