compute_posterior_intervals.BayesMallows

An object 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

Character string defining which parameter to compute
posterior intervals for. One of <code>"alpha"</code>, <code>"rho"</code>, or
<code>"cluster_probs"</code>. Default is <code>"alpha"</code>.

parameter

Decimal number in $[0,1]$ specifying the confidence level.
Defaults to <code>0.95</code>.

level

Integer specifying the number of decimals to include
in posterior intervals and the mean and median. Defaults to <code>3</code>.

decimals

other arguments passed to methods.

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

compute_posterior_intervals.BayesMallows function

An object 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>.

compute_posterior_intervals.BayesMallows: Compute posterior intervals

Description

Usage

Arguments

See Also