The Eigen-Distribution for Multi-Branching Weighted Trees on Independent Distributions

Weiguang Peng, Ning Ning Peng, Kazuyuki Tanaka

研究成果: Article査読

抄録

Okisaka et al. (2017) investigated the eigen-distribution for multi-branching trees weighted with (a,b) on correlated distributions, which is a weak version of Saks and Wigderson’s (1986) weighted trees. In the present work, we concentrate on the studies of eigen-distribution for multi-branching weighted trees on independent distributions. In particular, we generalize our previous results in Peng et al. (Inform Process Lett 125:41–45, 2017) to weighted trees where the cost of querying each leaf is associated with the leaf and its Boolean value. For a multi-branching weighted tree, we define a directional algorithm and show it is optimal among all the depth-first algorithms with respect to the given independent distribution. For some balanced multi-branching trees weighted with (a,b) on the assumption 0 < r < 1 (r is the probability that the root has value 0), we further prove that if an independent distribution d achieves the distributional complexity, then d turns out to be an independent and identical distribution.

本文言語English
ジャーナルMethodology and Computing in Applied Probability
DOI
出版ステータスAccepted/In press - 2021

ASJC Scopus subject areas

  • 統計学および確率
  • 数学 (全般)

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