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

被引：0
|
作者：
Ekhine Irurozki
Borja Calvo
Jose A. Lozano
机构：
[1] University of the Basque Country,Intelligent Systems Group, Computer Science School
来源：
Methodology and Computing in Applied Probability | 2018年 / 20卷
关键词：
Permutations; Mallows model; Sampling; Learning; Cayley distance; Cycle; Fisher-Yates-Knuth shuffle; 68Q32; 62D99; 62e10;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：1 / 35
页数：34
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