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New statistical method uses normalizing flows for likelihood-free inference

By PulseAugur Editorial · [1 sources] ·

A new statistical method has been developed for likelihood-free inference, particularly useful when dealing with nuisance parameters. This approach utilizes a neural-network-based normalizing flow to identify a pivotal statistic, which is shown to have minimal average Kullback–Leibler divergence of its p-values. The method can incorporate prior knowledge of invariances and demonstrates superior performance in terms of power and speed compared to existing techniques like the Welch test and profile likelihood-ratio methods on smaller sample sizes. AI

RANK_REASON The cluster contains a research paper detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

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

New statistical method uses normalizing flows for likelihood-free inference

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The cluster contains a research paper detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Phil Assheton ·

    Likelihood-free inference with nuisance parameters through normalizing flows

    arXiv:2609.10534v1 Announce Type: cross Abstract: We present a simple decomposition of a neural-network-based normalizing flow that naturally uncovers a pivotal statistic (or something close) in the presence of nuisance parameters, based only on a sample generator from the distri…

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