extrapolation-limit

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Verify the n·log n unseen-prediction horizon and its 'best possible' optimality against Orlitsky, Suresh & Wu (PNAS 2016) and Valiant & Valiant

capture promoted 2026-08-25 · statistics-of-the-unseen, good-turing, extrapolation-limit, information-theory, primary-source-verification
Orlitsky, Suresh & Wu's n·log n unseen-prediction horizon is proven optimal by a matched minimax lower bound, not just an achievability result

claim seedling Tier 1 2026-08-25 · statistics-of-the-unseen, good-turing, extrapolation-limit, information-theory, minimax-lower-bound
Valiant & Valiant's real, non-concurrent antecedent to the n·log n family is their own 2011 STOC paper, proving a matching O(n/log n) sample complexity for entropy and support-size estimation — a different problem than species-count prediction

claim seedling Tier 1 2026-08-25 · statistics-of-the-unseen, extrapolation-limit, information-theory, sample-complexity, priority-and-lineage
Valiant & Valiant's 2015 estimator independently reaches the same n·log n extrapolation range as Orlitsky, Suresh & Wu, but by an error metric the rival paper calls exponentially weaker in the hardest regime

claim seedling Tier 1 2026-08-25 · statistics-of-the-unseen, extrapolation-limit, information-theory, sample-complexity, priority-and-lineage
From n samples, the unseen can be predicted only about n·log n observations further, and that horizon is provably the best possible (Orlitsky, Suresh & Wu 2016)

claim seedling Tier 1 2026-07-12 · statistics-of-the-unseen, good-turing, extrapolation-limit, completeness, information-theory
Verify the n·log n unseen-prediction horizon and its 'best possible' optimality against Orlitsky, Suresh & Wu (PNAS 2016) and Valiant & Valiant

question answered 2026-07-12 · verification, statistics-of-the-unseen, extrapolation-limit, information-theory, source-criticism