We describe the application of model inference based on reference priors to two concrete examples in high energy physics: the determination of the CKM matrix parameters rhobar and etabar and the determination of the parameters m_0 and m_1/2 in a simplified version of the CMSSM SUSY model. We show how a 1-dimensional reference posterior can be mapped to the n-dimensional (n-D) parameter space of the given class of models, under a minimal set of conditions on the n-D function. This reference-based function can be used as a prior for the next iteration of inference, using Bayes' theorem recursively
The design of models which are appropriate for specific tasks is an important activity in machine le...
© 2015 Elsevier B.V. All rights reserved. Bayesian estimators are developed and compared with the ma...
Bayesian model calibration has become a powerful tool for the analysis of experimental data coupled ...
We describe the application of model inference based on reference priors to two concrete examples in...
The interpretation of data in terms of multi-parameter models of new physics, using the Bayesian app...
We develop reference analysis for matrix-variate dynamic models with unknown observation covariance ...
The paper derives the reference prior for complex covariance matrices. The reference prior is a noni...
What is the best way to exploit extra data -- be it unlabeled data from the same task, or labeled da...
The reference priors, initiated in Bernardo (1979) and further developed in Berger and Bernardo (199...
The combination of graphical models and reference analysis represents a powerful tool for Bayesian i...
The reference prior algorithm (Berger and Bernardo 1992) is applied to multivariate location-scale m...
We propose and illustrate a novel approach for deriving reference priors when the statistical model...
A key sticking point of Bayesian analysis is the choice of prior distribution, and there is a vast l...
When mapping is formulated in a Bayesian framework, the need of specifying a prior for the environme...
An important challenge in analyzing high dimensional data in regression settings is that of facing a...
The design of models which are appropriate for specific tasks is an important activity in machine le...
© 2015 Elsevier B.V. All rights reserved. Bayesian estimators are developed and compared with the ma...
Bayesian model calibration has become a powerful tool for the analysis of experimental data coupled ...
We describe the application of model inference based on reference priors to two concrete examples in...
The interpretation of data in terms of multi-parameter models of new physics, using the Bayesian app...
We develop reference analysis for matrix-variate dynamic models with unknown observation covariance ...
The paper derives the reference prior for complex covariance matrices. The reference prior is a noni...
What is the best way to exploit extra data -- be it unlabeled data from the same task, or labeled da...
The reference priors, initiated in Bernardo (1979) and further developed in Berger and Bernardo (199...
The combination of graphical models and reference analysis represents a powerful tool for Bayesian i...
The reference prior algorithm (Berger and Bernardo 1992) is applied to multivariate location-scale m...
We propose and illustrate a novel approach for deriving reference priors when the statistical model...
A key sticking point of Bayesian analysis is the choice of prior distribution, and there is a vast l...
When mapping is formulated in a Bayesian framework, the need of specifying a prior for the environme...
An important challenge in analyzing high dimensional data in regression settings is that of facing a...
The design of models which are appropriate for specific tasks is an important activity in machine le...
© 2015 Elsevier B.V. All rights reserved. Bayesian estimators are developed and compared with the ma...
Bayesian model calibration has become a powerful tool for the analysis of experimental data coupled ...