Bayesian Deck-of-cards-based Ordinal Regression with Sequential Preference Elicitation
The Deck-of-cards-based Ordinal Regression (DOR) infers a value function from a ranking of reference alternatives in which the Decision Maker (DM) inserts blank cards between consecutive levels to express preference intensity. DOR, and its stochastic extension (SMAA-DOR), treat these answers as hard constraints defining a set of compatible value functions. We propose B-DOR, a probabilistic reformulation of DOR in which each pair of adjacent levels yields an ordinal observation, the declared direction and the number of cards, modelled through a cumulative-link likelihood that relates the number of blank cards to the latent value difference between alternatives. Two Bayesian inference algor...
| Authors | Marco Grillo, Silvano Zappalà |
|---|---|
| Primary category | stat.ML |
| Published | 19 September 2026 |
| Last updated at source | 29 September 2026 |
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