A Unified Treatment of Predictive Model Comparison
Methodology
2015-06-09 v1
Abstract
The predictive performance of any inferential model is critical to its practical success, but quantifying predictive performance is a subtle statistical problem. In this paper I show how the natural structure of any inferential problem defines a canonical measure of relative predictive performance and then demonstrate how approximations of this measure yield many of the model comparison techniques popular in statistics and machine learning.
Keywords
Cite
@article{arxiv.1506.02273,
title = {A Unified Treatment of Predictive Model Comparison},
author = {Michael Betancourt},
journal= {arXiv preprint arXiv:1506.02273},
year = {2015}
}
Comments
20 pages, 11 figures