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New research explores gap between Bayesian updates and predictive accuracy

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv explores the relationship between accurate posterior prediction and the approximation of Bayesian updates. The research demonstrates that a significant gap between update maps can coexist with vanishing predictive KL divergence for any fixed K. The study introduces a deterministic radial filter that, along with exact Bayesian mixing, operates on belief coordinates. As a parameter q approaches zero, the separation between these filters can grow linearly with confidence scale, while their categorical KL divergence diminishes. AI

IMPACT This research may refine understanding of model update mechanisms and their impact on predictive performance.

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research explores gap between Bayesian updates and predictive accuracy

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The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Qifu Wen, Shuaijun Liu, Zihan Zhou, Xi Zeng, Ningxin Su ·

    How Wrong Can a Good Predictor Be? Diverging Updates with Vanishing Predictive KL

    arXiv:2609.11132v1 Announce Type: cross Abstract: Accurate posterior prediction need not require accurate approximation of Bayesian updates. We prove that an unbounded gap between the update maps can coexist with vanishing predictive KL for every fixed finite $K\ge2$ in a station…

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