Bayesian Modeling via Goodness-of-fit
Methodology
2018-04-18 v3 Statistics Theory
Machine Learning
Statistics Theory
Abstract
The two key issues of modern Bayesian statistics are: (i) establishing principled approach for distilling statistical prior that is consistent with the given data from an initial believable scientific prior; and (ii) development of a Bayes-frequentist consolidated data analysis workflow that is more effective than either of the two separately. In this paper, we propose the idea of "Bayes via goodness of fit" as a framework for exploring these fundamental questions, in a way that is general enough to embrace almost all of the familiar probability models. Several illustrative examples show the benefit of this new point of view as a practical data analysis tool. Relationship with other Bayesian cultures is also discussed.
Cite
@article{arxiv.1802.00474,
title = {Bayesian Modeling via Goodness-of-fit},
author = {Subhadeep and Mukhopadhyay and Douglas Fletcher},
journal= {arXiv preprint arXiv:1802.00474},
year = {2018}
}
Comments
Revised version