smc_mallows_new_users_partial_alpha_fixed

Matrix containing the full set of observed rankings of size n_assessors by n_items

R_obs

Integer is the number of items in a ranking

n_items

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

leap_size Integer specifying the step size of the leap-and-shift
proposal distribution

leap_size

Integer specifying the number of particles

Integer specifying the number of time steps in the SMC algorithm

Time

Estimate of the partition function, computed with
<code><a rd-options="" href="/link/estimate_partition_function?package=BayesMallows&version=1.1.0" data-mini-rdoc="BayesMallows::estimate_partition_function">estimate_partition_function</a></code> in the BayesMallow R package estimate_partition_function.

logz_estimate

Integer value for the number of applications we apply the MCMC move kernel

mcmc_kernel_app

Integer value for the number of new observations (complete rankings) for each time step

num_new_obs

A character string specifying the approach for filling in the missing data, options are "pseudolikelihood" or "random"

aug_method

A numeric value of the scale parameter which is known and fixed

alpha

Function to perform resample-move SMC algorithm where we receive new users with complete rankings
at each time step

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>).

Oystein Sorensen

BayesMallows

Bayesian Preference Learning with the Mallows Rank Model

Valeria Vitelli

Marta Crispino

Qinghua Liu

Cristina Mollica

Luca Tardella

Anja Stein

Waldir Leoncio

smc_mallows_new_users_partial_alpha_fixed function

Estimate of the partition function, computed with
<code><a rd-options='' href='estimate_partition_function'>estimate_partition_function</a></code> in the BayesMallow R package estimate_partition_function.

smc_mallows_new_users_partial_alpha_fixed: SMC-mallows new users partial (alpha fixed)

Description

Usage

Arguments

Value