correction_kernel

Function to determine if the augmented ranking is compatible
with the new observed partial ranking. If it is not, the we create a new
augmentation using the random sampling approach and calculate the
augmentation probability.

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>;
Sorensen et al., R Journal, 2020 <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 <doi:10.1214/15-AOS1389>).

Oystein Sorensen

BayesMallows

Bayesian Preference Learning with the Mallows Rank Model

Waldir Leoncio

Valeria Vitelli

Marta Crispino

Qinghua Liu

Cristina Mollica

Luca Tardella

Anja Stein

correction_kernel function

<dl><dt>observed_ranking</dt>
<dd>A ranking sequence vector of the observed partial
ranking (no missing values) The original incomplete partial ranking
is in the rankings data set.</dd>
<dt>current_ranking</dt>
<dd>A ranking sequence vector of the current augmented
ranking (no missing values)</dd>
<dt>n_items</dt>
<dd>Integer is the number of items in a ranking</dd></dl>

Arguments

Correction Kernel — correction_kernel

<dl>

<dt>observed_ranking</dt>
<dd>A ranking sequence vector of the observed partial
ranking (no missing values) The original incomplete partial ranking
is in the rankings data set.</dd>


<dt>current_ranking</dt>
<dd>A ranking sequence vector of the current augmented
ranking (no missing values)</dd>


<dt>n_items</dt>
<dd>Integer is the number of items in a ranking</dd>

</dl>

Correction Kernel

correction_kernel: Correction Kernel

Description

Usage

Value

Arguments