BayesMallowsSMC2: Nested Sequential Monte Carlo for the Bayesian Mallows Model
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