From Frequentist to Bayesian Contextual Optimization
Bayesian contextual optimization (BCO) is proposed, a framework that maintains a Gibbs posterior over the parameter space that weights candidate models by their empirical decision quality rather than statistical fit, thereby avoiding commitment to a potentially misspecified likelihood while encoding the full distributi...