BayesMallows-package: BayesMallows: Bayesian Preference Learning with the Mallows...

In BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

Description

An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 https://jmlr.org/papers/v18/15-481.html; Crispino et al., Annals of Applied Statistics, 2019 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/18-AOAS1203")}; Sorensen et al., R Journal, 2020 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.32614/RJ-2020-026")}; Stein, PhD Thesis, 2023 https://eprints.lancs.ac.uk/id/eprint/195759). Both Metropolis-Hastings and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule, Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank model, both with the importance sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/15-AOS1389")}).

Author(s)

Maintainer: Oystein Sorensen [e-mail] (ORCID)

Authors:

• Waldir Leoncio [e-mail]

• Valeria Vitelli [e-mail] (ORCID)

• Marta Crispino [e-mail]

• Qinghua Liu [e-mail]

• Cristina Mollica [e-mail]

• Luca Tardella

• Anja Stein

References

\insertRef

sorensen2020BayesMallows

See Also

Useful links:

• https://github.com/ocbe-uio/BayesMallows

• https://ocbe-uio.github.io/BayesMallows/

• Report bugs at https://github.com/ocbe-uio/BayesMallows/issues

BayesMallows documentation built on Sept. 11, 2024, 5:31 p.m.