log_expected_dist

alpha

n_items

Number of occurrences for each unique distance.
Applicable for Footrule and Spearman distance.

cardinalities

A string. Available options are <code>"ulam"</code>, <code>"footrule"</code> and <code>"spearman"</code>.

metric

Compute the logarithm of the expected distance of metrics for a Mallows rank model

internal

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

log_expected_dist function

Compute the logarithm of the expected distance of metrics for a Mallows rank model — log_expected_dist

Compute the logarithm of the expected distance of metrics for a Mallows rank model

log_expected_dist: Compute the logarithm of the expected distance of metrics for a Mallows rank model

Description

Usage

Arguments

Value