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New method provides empirical concentration bounds for infinite-memory chains

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for establishing empirical concentration bounds for stationary chains with infinite memory, extending beyond the traditional Markov chain framework. This approach estimates unknown dependence quantities from a single trajectory, applicable to both finite and continuous state spaces. The derived confidence bounds are asymptotically as tight as theoretical ones and remain adaptive, not requiring prior knowledge of constants. AI

IMPACT This research could improve the reliability of learning algorithms by providing tighter confidence bounds for time series analysis.

RANK_REASON The cluster contains a single academic paper detailing a new statistical method. [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 method provides empirical concentration bounds for infinite-memory chains

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The cluster contains a single academic paper detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Pierre Alquier, Paul Doukhan ·

    Empirical concentration for chains with infinite memory

    arXiv:2610.32265v1 Announce Type: new Abstract: Concentration inequalities for time series usually involve unknown quantities measuring dependence. This prevents their direct use as confidence bounds, or as criteria for learning algorithms. Recent works proved empirical concentra…

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