Sampling and Learning Mallows and Generalized Mallows Models Under the Cayley Distance
Sampling and Learning Mallows and Generalized Mallows Models Under the Cayley Distance
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DOI：
10.1007/s11009-016-9506-7
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发表时间：
2016-06
影响因子：
0.9
通讯作者：
Ekhine Irurozki;Borja Calvo;J. A. Lozano
中科院分区：
文献类型：
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作者：
Ekhine Irurozki;Borja Calvo;J. A. Lozano
马洛模型和广义马洛模型是表示排列空间上的概率分布的紧凑而强大和自然的方法。在本文中，我们处理的问题，采样和学习这样的分布时，排列的度量是凯莱距离。我们提出了新的方法，这两个操作，并通过几个实验显示其性能。在生物学领域的应用，以激发这种模式的兴趣。
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.