BayesMallowsSMC2: Nested Sequential Monte Carlo for the Bayesian Mallows Model

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Authors: Øystein Sørensen
Cited by: 0
References: 1

Abstract

Provides nested sequential Monte Carlo algorithms for performing sequential inference in the Bayesian Mallows model, which is a widely used probability model for rank and preference data. The package implements the SMC2 (Sequential Monte Carlo Squared) algorithm for handling sequentially arriving rankings and pairwise preferences, including support for complete rankings, partial rankings, and pairwise comparisons. The methods are based on Sorensen (2025) .

Fields of Study & Subjects

Markov Chains and Monte Carlo Methods Probability and Statistical Research Bayesian Modeling and Causal Inference

Publication details

Publication date
3 February 2026
Document type
Dataset