Matlab/Octave package: BNPPL
Bayesian nonparametric Plackett-Luce for ranking data
to the demo (html)
This Matlab/Octave package implements sampling algorithms for
simulation and Bayesian inference in (mixture of) nonparametric
Plackett-Luce models (Caron
et al., 2014). These models are useful for analysing partial
ranking data consisting of ordered lists of top-m items among a very
large, potentially unbounded set of items.
The package has been tested on Matlab
R2014a (with statistics toolbox) and Octave 3.6.4.
F. Caron, Y.W. Teh, T.B. Murphy. Bayesian nonparametric
Plackett-Luce models for the analysis of preferences for college degree
programmes. The Annals of Applied Statistics, vol. 8, no2, pp. 1145-1181,
2014. Download paper.
of the package
Sampler and inference in
- demo_nppl.m: reproduces
some figures of the paper and provides a demo of the functions in the
package - see the htlm version of the script
Sampler and inference in mixture of
- sample_nppl.m: samples
from the nonparametric Plackett-Luce model
- mcmc_nppl.m: runs a MCMC
algorithm for the nonparametric Plackett-Luce model
Processing of the outputs of the MCMC
- sample_mixnppl.m: samples
from the nonparametric mixture of Plackett-Luce model
- mcmc_mixnppl.m: runs a
MCMC algorithm for the nonparametric mixture of Plackett-Luce model
- coclustering.m: computes
the coclustering/similarity matrix from the output of the MCMC algorithm
- clust_est_binder.m: gets
a Bayesian point estimate of the partition from the MCMC output using
Binder loss function
Last update: 04-07-2014. Correction of a bug in clust_est_binder.m
(C) Copyright 2014 François
Caron,University of Oxford
Permission is granted for anyone to copy, use, or modify these programs and accompanying documents for purposes of research or education,
provided this copyright notice is retained, and note is made of any changes that have been made.
These programs and documents are distributed without any warranty, express or implied.
As the programs were written for research purposes only, they have not been tested to the degree that would be advisable in any important application.
All use of these programs is entirely at the user's own risk.