Harmonic mean, random polynomials and stochastic matrices.

DOI10.1016/S0196-8858(03)00023-XzbMath1038.15014arXivmath/0105236MaRDI QIDQ1415390

Igor Rivin, Natalia L. Komarova

Publication date: 3 December 2003

Published in: Advances in Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/math/0105236

zbMATH Keywords

learning theoryMarkov processeslaw of large numbersconvergence ratelearning processlarge deviationsstochastic matricesstable lawlimiting distributionrandom polynomialsharmonic meandominant eigenvaluememoryless learner algorithmsmallest critical point

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Large deviations (60F10) Means (26E60) Stochastic matrices (15B51)

Related Items (8)

Weak log-majorization, Mahler measure and polynomial inequalities ⋮ Expectations of large data means ⋮ On the local pairing behavior of critical points and roots of random polynomials ⋮ Evolutionary models of color categorization based on discrimination ⋮ Pairing between zeros and critical points of random polynomials with independent roots ⋮ The Moment Zeta Function and Applications ⋮ Higher-order, polar and Sz.-Nagy's generalized derivatives of random polynomials with independent and identically distributed zeros on the unit circle ⋮ A generalized transmuted uniform distribution

Cites Work

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• Evolution of Universal Grammar
• A Generalization of a Theorem of Bôcher
• On the Rate of Convergence to a Stable Law

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