predict_top_k

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

model_fit

A numeric value specifying the number of iterations to discard
as burn-in. Defaults to <code>model_fit$burnin</code>, and must be provided if
<code>model_fit$burnin</code> does not exist. See
<code><a rd-options="" href="/link/assess_convergence?package=BayesMallows&version=1.1.0" data-mini-rdoc="BayesMallows::assess_convergence">assess_convergence</a></code>.

burnin

Integer specifying the k in top-$k$.

Predict the posterior probability, per item, of being ranked among the
top-$k$ for each assessor. This is useful when the data take the form of
pairwise preferences.

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

predict_top_k function

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

A numeric value specifying the number of iterations to discard
as burn-in. Defaults to <code>model_fit$burnin</code>, and must be provided if
<code>model_fit$burnin</code> does not exist. See
<code><a rd-options='' href='assess_convergence'>assess_convergence</a></code>.

predict_top_k: Predict Top-k Rankings with Pairwise Preferences

Description

Usage

Arguments

Value

See Also