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

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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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Authors and Affiliations

  1. Intelligent Systems Group, Computer Science School, University of the Basque Country, Donostia – San Sebastian, Spain

    Ekhine Irurozki, Borja Calvo & Jose A. Lozano

Authors
  1. Ekhine Irurozki
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  2. Borja Calvo
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  3. Jose A. Lozano
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Correspondence to Ekhine Irurozki.

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Irurozki, E., Calvo, B. & Lozano, J.A. Sampling and Learning Mallows and Generalized Mallows Models Under the Cayley Distance. Methodol Comput Appl Probab 20, 1–35 (2018). https://doi.org/10.1007/s11009-016-9506-7

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  • DOI: https://doi.org/10.1007/s11009-016-9506-7

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