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

Ekhine Irurozki; Borja Calvo; Jose A. Lozano

doi:10.1007/s11009-016-9506-7

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

Ekhine Irurozki
, Borja Calvo
, Jose A. Lozano

University of the Basque Country

Research output: Contribution to journal › Article › peer-review

18 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	1-35
Number of pages	35
Journal	Methodology and Computing in Applied Probability
Volume	20
Issue number	1
DOIs	https://doi.org/10.1007/s11009-016-9506-7
Publication status	Published - 1 Mar 2018
Externally published	Yes

Keywords

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

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abstract = "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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KW - Fisher-Yates-Knuth shuffle

KW - Learning

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KW - Sampling

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

Abstract

Keywords

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