print.BayesMallowsMixtures

An object of type <code>BayesMallowsMixtures</code>, returned from
<code><a rd-options="" href="/link/compute_mallows_mixtures?package=BayesMallows&version=1.1.0" data-mini-rdoc="BayesMallows::compute_mallows_mixtures">compute_mallows_mixtures</a></code>.

Other arguments passed to <code>print</code> (not used).

The default print method for a <code>BayesMallowsMixtures</code> object.

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 <doi:10.1214/18-AOAS1203>). 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 <doi:10.1214/15-AOS1389>).

Oystein Sorensen

BayesMallows

Bayesian Preference Learning with the Mallows Rank Model

Valeria Vitelli

Marta Crispino

Qinghua Liu

Cristina Mollica

Luca Tardella

Anja Stein

Waldir Leoncio

print.BayesMallowsMixtures function

An object of type <code>BayesMallowsMixtures</code>, returned from
<code><a rd-options='' href='compute_mallows_mixtures'>compute_mallows_mixtures</a></code>.

print.BayesMallowsMixtures: Print Method for BayesMallowsMixtures Objects

Description

Usage

Arguments