sample_dataset

<p>A synthetic 3D matrix (<code>n_users</code>, <code>n_items</code>, <code>Time</code>) generated
using the sample_mallows function. These are test datasets used to run
the SMC-Mallows framework for the cases where we know all of the users
in our system and their original ranking information are partial rankings.
However at some point in time, we observe extra information about
an existing user in the form of a rank for an item that was previously
not known (<code>NA</code>). These datasets are very contrived as the first
time step (<code>sample_dataset[, , 1]</code>) we observed the top <code>m / 2</code>
items from each user, where <code>m</code> is the number of items in a ranking.
Then, as we increase the time, we observe the next top ranked item from
one user at a time, then the next top ranked item, and so on until we have
a complete dataset at <code>sample_dataset[, , Time]</code>.</p>

datasets

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

sample_dataset function

<p>An object of class <code>array</code> of dimension 10 x 6 x 31.</p>

Format

A synthetic 3D matrix generated using the sample_mallows function — sample_dataset

A synthetic 3D matrix generated using the sample_mallows function

sample_dataset: A synthetic 3D matrix generated using the sample_mallows function

Description

Usage

Arguments

Format

References

See Also