compute_importance_sampling_estimate

Vector of alpha values at which to compute partition function.

alpha_vector

Integer specifying the number of ranked items.

n_items

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

metric

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

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 <http://jmlr.org/papers/v18/15-481.html>; Crispino et al., to appear in Annals of Applied Statistics). Both 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>).

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

Description

Usage

Arguments