Regression and Bayesian Methods in Modern Preference Elicitation

Application to simple smoothie-making

Linear regression is often considered the workhorse of predictive modeling, yet its application extends beyond straightforward predictive tasks. This article seeks to enrich the dialogue around regression techniques by introducing Probit Linear Regression as an effective tool for modeling preferences. Furthermore, we employ a Bayesian framework to transition from classical to Bayesian Linear Regression, elucidating the intrinsic relationship between cost-based optimization — specifically Binary Cross-Entropy (BCE) loss minimization — and …

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