Backend
Recovering Dirichlet coefficients from even samples
arXiv stat.TH0 views
arXiv:2610.24174v1 Announce Type: cross Abstract: In this work, we determine the exact case error for recovering a Dirichlet coefficient from finitely many positive even samples with bounded additive errors. For sequences with a bounded tail and an unrestricted initial block, a polynomial estimator is minimax among all recovery maps. Its tail and noise constants admit explicit product formulas. We prove a sharp tail asymptotic on the index scale K^(3/2) and give a terminating procedure for selecting the sample count from a computable error bound.
Source:https://arxiv.org/abs/2610.24174PreviousMargin Certificates for Voting Classifiers under Mixing DependenceNextThe Geometry of Informativeness: A Bregman-Centroid Diagnostic for Partial Pooling in Multisite Trials
Related posts

Dr Amr Eloufy: How to Save Our Hair in This Fast-Moving World
0dev.to Watercooler
Only 6 of 45 Odds APIs Cover All 8 Big US Sportsbooks
0dev.to Data
Hadoop Ecosystem Overview
0dev.to Data
From One Chargeback to a Fraud Ring: Building Link Analysis That Works
0dev.to Data

Choosing the right AI web scraper depends on your needs, budget, and technical skills. This guide makes comparing the top tools easier and helps you find the right fit for your next project.
0dev.to Discuss

Choosing the right AI web scraper depends on your needs, budget, and technical skills. This comparison makes it easier to understand which tools are worth trying, from no-code options to developer-foc
0dev.to Discuss