scholarly journals Linear Extensions and Comparable Pairs in Partial Orders

Order ◽  
2017 ◽  
Vol 35 (3) ◽  
pp. 403-420 ◽  
Author(s):  
Colin McDiarmid ◽  
David Penman ◽  
Vasileios Iliopoulos
Author(s):  
Ao Liu ◽  
Zhibing Zhao ◽  
Chao Liao ◽  
Pinyan Lu ◽  
Lirong Xia

We propose an EM-based framework for learning Plackett-Luce model and its mixtures from partial orders. The core of our framework is the efficient sampling of linear extensions of partial orders under Plackett-Luce model. We propose two Markov Chain Monte Carlo (MCMC) samplers: Gibbs sampler and the generalized repeated insertion method tuned by MCMC (GRIM-MCMC), and prove the efficiency of GRIM-MCMC for a large class of preferences.Experiments on synthetic data show that the algorithm with Gibbs sampler outperforms that with GRIM-MCMC. Experiments on real-world data show that the likelihood of test dataset increases when (i) partial orders provide more information; or (ii) the number of components in mixtures of PlackettLuce model increases.


2012 ◽  
Vol 58 (6) ◽  
pp. 417-423 ◽  
Author(s):  
Emanuele Frittaion ◽  
Alberto Marcone

1980 ◽  
Vol 1 (4) ◽  
pp. 405-410 ◽  
Author(s):  
F. R. K. Chung ◽  
P. C. Fishburn ◽  
R. L. Graham

Chemosphere ◽  
2003 ◽  
Vol 53 (8) ◽  
pp. 981-992 ◽  
Author(s):  
Dorte Lerche ◽  
Peter B. Sørensen

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