mlsanityData to 14 Oct 2026

Assigning a value to a power likelihood in a general Bayesian model

arXiv 1701.08515 ↗First posted 30 Jan 2017stat.MEcited by 46

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Abstract (arXiv)

Bayesian approaches to data analysis and machine learning are widespread and popular as they provide intuitive yet rigorous axioms for learning from data; see Bernardo and Smith (2004) and Bishop (2006). However, this rigour comes with a caveat that the Bayesian model is a precise reflection of Nature. There has been a recent trend to address potential model misspecification by raising the likelihood function to a power, primarily for robustness reasons, though not exclusively. In this paper we provide a coherent specification of the power parameter once the Bayesian model has been specified in the absence of a perfect model.

Key facts. Cited by 46 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Jan 2017.

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Citing papers per year: 2021: 3; 2022: 5; 2023: 7; 2024: 10; 2025: 8; 2026: 13.

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