lsamvy面积近似的布朗桥展开式和Riemann zeta函数的特殊值

IF 0.9 4区 数学 Q3 COMPUTER SCIENCE, THEORY & METHODS Combinatorics, Probability & Computing Pub Date : 2021-02-19 DOI:10.1017/S096354832200030X
James Foster, Karen Habermann
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引用次数: 9

摘要

本文研究了基于傅里叶级数展开式和相关布朗桥的多项式展开式的布朗运动lvmy面积的近似。比较lsamvy面积近似的渐近收敛速率,我们看到由布朗桥的多项式展开产生的近似比Kloeden-Platen-Wright近似更精确,同时仍然只使用独立的正态随机向量。然后我们将这些近似的渐近收敛率与相应的布朗桥级数展开式的极限涨落联系起来。此外,我们用于识别布朗桥的karhunen - lo和傅立叶级数展开式的波动过程的分析本身也很有趣,我们对其进行了扩展,以给出偶数正整数处黎曼ζ函数值的独立推导。
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Brownian bridge expansions for Lévy area approximations and particular values of the Riemann zeta function
We study approximations for the Lévy area of Brownian motion which are based on the Fourier series expansion and a polynomial expansion of the associated Brownian bridge. Comparing the asymptotic convergence rates of the Lévy area approximations, we see that the approximation resulting from the polynomial expansion of the Brownian bridge is more accurate than the Kloeden–Platen–Wright approximation, whilst still only using independent normal random vectors. We then link the asymptotic convergence rates of these approximations to the limiting fluctuations for the corresponding series expansions of the Brownian bridge. Moreover, and of interest in its own right, the analysis we use to identify the fluctuation processes for the Karhunen–Loève and Fourier series expansions of the Brownian bridge is extended to give a stand-alone derivation of the values of the Riemann zeta function at even positive integers.
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来源期刊
Combinatorics, Probability & Computing
Combinatorics, Probability & Computing 数学-计算机:理论方法
CiteScore
2.40
自引率
11.10%
发文量
33
审稿时长
6-12 weeks
期刊介绍: Published bimonthly, Combinatorics, Probability & Computing is devoted to the three areas of combinatorics, probability theory and theoretical computer science. Topics covered include classical and algebraic graph theory, extremal set theory, matroid theory, probabilistic methods and random combinatorial structures; combinatorial probability and limit theorems for random combinatorial structures; the theory of algorithms (including complexity theory), randomised algorithms, probabilistic analysis of algorithms, computational learning theory and optimisation.
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