compute_consensus.consensus_SMCMallows

Compute the consensus ranking using either cumulative probability (CP) or maximum a posteriori (MAP) consensus
vitelli2018BayesMallows. For mixture models, the
consensus is given for each mixture.

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_consensus.consensus_SMCMallows function

<dl><dt>model_fit</dt>
<dd>An object returned from <code>compute_mallows</code>.</dd>
<dt>type</dt>
<dd>Character string specifying which consensus to compute. Either
<code>"CP"</code> or <code>"MAP"</code>. Defaults to <code>"CP"</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>...</dt>
<dd>other arguments passed to methods.</dd></dl>

Arguments

Author

Compute Consensus Ranking — compute_consensus.consensus_SMCMallows

<dl>

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


<dt>type</dt>
<dd>Character string specifying which consensus to compute. Either
<code>"CP"</code> or <code>"MAP"</code>. Defaults to <code>"CP"</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>...</dt>
<dd>other arguments passed to methods.</dd>

</dl>

Compute Consensus Ranking

compute_consensus.consensus_SMCMallows: Compute Consensus Ranking

Description

Usage

Arguments

Author