compute_importance_sampling_estimate

Compute importance sampling estimates of log partition function
for footrule and Spearman distances.

internal

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_importance_sampling_estimate function

<dl><dt>alpha_vector</dt>
<dd>Vector of alpha values at which to compute partition function.</dd>
<dt>n_items</dt>
<dd>Integer specifying the number of ranked items.</dd>
<dt>metric</dt>
<dd>Distance measure of the target Mallows distribution. Defaults to <code>footrule</code>.</dd>
<dt>nmc</dt>
<dd>Number of Monte Carlo samples. Defaults to <code>1e4</code>.</dd></dl>

Arguments

Compute importance sampling estimates of log partition function
for footrule and Spearman distances. — compute_importance_sampling_estimate

<dl>

<dt>alpha_vector</dt>
<dd>Vector of alpha values at which to compute partition function.</dd>


<dt>n_items</dt>
<dd>Integer specifying the number of ranked items.</dd>


<dt>metric</dt>
<dd>Distance measure of the target Mallows distribution. Defaults to <code>footrule</code>.</dd>


<dt>nmc</dt>
<dd>Number of Monte Carlo samples. Defaults to <code>1e4</code>.</dd>

</dl>

Compute importance sampling estimates of log partition function
for footrule and Spearman distances.

compute_importance_sampling_estimate: Compute importance sampling estimates of log partition function for footrule and Spearman distances.

Description

Usage

Arguments