Nathaniel Haines
Nathaniel Haines is a Bayesian, a data scientist, and a psychologist (mostly in that order). His work touches multiple fields including computational statistics, cognitive science, and actuarial science, and he is a core contributor to open-source Python and R libraries that make advanced Bayesian modeling techniques more accessible (BayesBlend and hBayesDM). He currently works as the Manager of Data Science Research at Ledger Investing, a Y Combinator backed fintech building a marketplace for casualty insurance-linked securities.
Sessions
This talk introduces BayesBlend, a new, open-source Python package designed to simplify model blending using pseudo-Bayesian model averaging, stacking, and hierarchical stacking. BayesBlend enables users to improve out-of-sample predictive performance by blending predictions from multiple competing models, which is particularly useful in M-open settings where the true model is unknown. The talk will include practical examples from insurance loss modeling.