asymptotic_partition_function

alpha_vector

Integer specifying the number of items.

n_items

One of <code>"footrule"</code> and <code>"spearman"</code>.

metric

Integer specifying the number of iterations.

n_iterations

Compute the asymptotic approximation of the logarithm of the partition function,
using the iteration algorithm of mukherjee2016;textualBayesMallows.

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>).

asymptotic_partition_function: Asymptotic Approximation of Partition Function

Description

Usage

Arguments

Value

References