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arXiv Machine Learning By Yujie Chen, Antik Chakraborty, Anindya Bhadra

Covariate-dependent Joint Modeling of Multivariate Ordinal Preferences and Its Connections with Comparison Models

Read the original on arXiv Machine Learning →

The paper introduces a covariate‑dependent joint modeling framework for multivariate ordinal preference data, addressing limitations of existing methods that treat attributes separately or reduce observations to pairwise comparisons. It proposes a consecutive‑ratio Markov random field model that retains the full ordinal structure and demonstrates that standard comparison models emerge as special cases under restrictive assumptions. The authors also present a maximum‑likelihood inference procedure capable of handling an intractable normalizer.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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