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Closed-Form Expressions for the Normalizing Constants of the Mallows Model and Weighted Mallows Model on Combinatorial Domains

Author

Listed:
  • Jean-Pierre van Zyl

    (Division of Computer Science, Stellenbosch University, Stellenbosch 7599, South Africa)

  • Andries Petrus Engelbrecht

    (Division of Computer Science, Stellenbosch University, Stellenbosch 7599, South Africa
    Department of Industrial Engineering, Stellenbosch University, Stellenbosch 7599, South Africa
    Center for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, Mubarak Al-Abdullah 7207, Kuwait)

Abstract

This paper expands the Mallows model for use in combinatorial domains. The Mallows model is a popular distribution used to sample permutations around a central tendency but requires a unique normalizing constant for each distance metric used in order to be computationally efficient. In this paper, closed-form expressions for the Mallows model normalizing constant are derived for the Hamming distance, symmetric difference, and the similarity coefficient in combinatorial domains. Additionally, closed-form expressions are derived for the normalizing constant of the weighted Mallows model in combinatorial domains. The weighted Mallows model increases the versatility of the Mallows model by allowing granular control over likelihoods of individual components in the domain. The derivation of the closed-form expression results in a reduction of the order of calculations required to calculate probabilities from exponential to constant.

Suggested Citation

  • Jean-Pierre van Zyl & Andries Petrus Engelbrecht, 2025. "Closed-Form Expressions for the Normalizing Constants of the Mallows Model and Weighted Mallows Model on Combinatorial Domains," Mathematics, MDPI, vol. 13(19), pages 1-18, September.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:19:p:3126-:d:1762005
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    References listed on IDEAS

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    1. Jaewoo Park & Murali Haran, 2018. "Bayesian Inference in the Presence of Intractable Normalizing Functions," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1372-1390, July.
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