Collaboration Opportunity

[DLWCMD]

I have created a tool for evaluating the statistical significance/predictive reliability of ML/AI analysis and inference. My use case assumes that an analyst has identified an estimator and parameter values that he/she believes to be superior. My tool (working title mlcompare) then applies one or more “meta-tests” to confirm (or not) if the estimator’s claimed superiority is truly meaningful, that is, statistically significant.

The initial version of mlcompare used frequentist (aka p-val) methods to evaluate statistical significance, but I have since decided to replace them with Bayesian methods and, ultimately, causal analysis.

I am interested in experimenting with your package in hopes of collaborating with you to blend our packages (or at least elements of them) to create a tool that integrates the best of Bayesian inference with statistically based model/estimator comparisons.

I have attached the latest version of my “statement of need,” which term I noticed in your upcoming paper introducing bayes-toolbox. This document was created before my decision to move to a Bayesian foundation, but it presents the overall use case.

Thanks, and I look forward to hearing from you.

David L. Wilt [email protected] 1-540-420-0844

PDF_V5.5.pdf