compute_posterior_intervals.BayesMallows

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

compute_posterior_intervals.BayesMallows function

<dl><dt>model_fit</dt>
<dd>An object of class <code>BayesMallows</code> returned from
<code>compute_mallows</code>.</dd>
<dt>burnin</dt>
<dd>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>assess_convergence</code>.</dd>
<dt>parameter</dt>
<dd>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>.</dd>
<dt>level</dt>
<dd>Decimal number in $[0,1]$ specifying the confidence level.
Defaults to <code>0.95</code>.</dd>
<dt>decimals</dt>
<dd>Integer specifying the number of decimals to include in
posterior intervals and the mean and median. Defaults to <code>3</code>.</dd>
<dt>...</dt>
<dd>Other arguments. Currently not used.</dd></dl>

Arguments

Compute posterior intervals — compute_posterior_intervals.BayesMallows

<dl>

<dt>model_fit</dt>
<dd>An object of class <code>BayesMallows</code> returned from
<code>compute_mallows</code>.</dd>


<dt>burnin</dt>
<dd>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>assess_convergence</code>.</dd>


<dt>parameter</dt>
<dd>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>.</dd>


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


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


<dt>...</dt>
<dd>Other arguments. Currently not used.</dd>

</dl>

Compute posterior intervals

compute_posterior_intervals.BayesMallows: Compute posterior intervals

Description

Usage

Arguments

See Also