Sampling and Learning Mallows and Generalized Mallows Models Under the Cayley Distance

被引：18

作者：

Irurozki, Ekhine ^{[1
]}

Calvo, Borja ^{[1
]}

Lozano, Jose A. ^{[1
]}

机构：

[1] Univ Basque Country, Comp Sci Sch, Intelligent Syst Grp, Donostia San Sebastian, Spain

来源：

METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY | 2018年 / 20卷 / 01期

关键词：

Permutations; Mallows model; Sampling; Learning; Cayley distance; Cycle; Fisher-Yates-Knuth shuffle;

D O I：

10.1007/s11009-016-9506-7

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

c The Mallows and Generalized Mallows models are compact yet powerful and natural ways of representing a probability distribution over the space of permutations. In this paper, we deal with the problems of sampling and learning such distributions when the metric on permutations is the Cayley distance. We propose new methods for both operations, and their performance is shown through several experiments. An application in the field of biology is given to motivate the interest of this model.

引用

页码：1 / 35

页数：35

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