metropolis_hastings_aug_ranking_pseudo

Numeric value of the scale parameter

alpha

Numeric vector specifying the consensus ranking

Integer is the number of items in a ranking

n_items

An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs

partial_ranking

An complete rank sequence vector of the proposed augmented ranking obtained from calculate_forward_probability function

current_ranking

A character string specifying the distance metric to use in the
Bayesian Mallows Model. Available options are <code>"footrule"</code>,
<code>"spearman"</code>, <code>"cayley"</code>, <code>"hamming"</code>, <code>"kendall"</code>, and
<code>"ulam"</code>.

metric

Function to perform Metropolis-Hastings for new augmented ranking using the pseudolikelihood augmentation approach

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>). 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>).

metropolis_hastings_aug_ranking_pseudo: Metropolis-Hastings Augmented Ranking (pseudolikelihood)

Description

Usage

Arguments

Value