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New statistical concept 'Transitional Conditional Independence' introduced

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This paper introduces a new concept called transitional conditional independence, designed to handle variables that are not random, such as parameters or treatments. Unlike traditional conditional independence, this new relation does not require a probability distribution on the non-stochastic input. It is defined by a specific factorization of a Markov kernel and is asymmetric, a property essential for its intended meaning. The research demonstrates that this concept can be applied to various statistical problems, including ancillarity, sufficiency, adequacy, invariant prediction, and Bayesian networks. AI

RANK_REASON The item is an academic paper detailing a new statistical concept. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

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New statistical concept 'Transitional Conditional Independence' introduced

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The item is an academic paper detailing a new statistical concept. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Patrick Forr\'e ·

    Transitional Conditional Independence

    arXiv:2104.11547v3 Announce Type: replace-cross Abstract: Statistical models contain variables that are not random: parameters, treatments, environments, design points. Ordinary conditional independence cannot express relations involving such variables. To apply it one must first…

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