trueskill

trueskill: Implementation the TrueSkill algorithm in R. An implementation of the TrueSkill algorithm (Herbrich, R., Minka, T. and Grapel, T) in R; a Bayesian skill rating system with inference by approximate message passing on a factor graph. Used by Xbox to rank gamers and identify appropriate matches. http://research.microsoft.com/en-us/projects/trueskill/default.aspx Current version allows for one player per team. Will update as time permits. Requires R version 3.0 as it is written with Reference Classes. URL: https://github.com/bhoung/trueskill-in-r Acknowledgements to Doug Zongker and Heungsub Lee for their python implementations of the algorithm and for the liberal reuse of Doug’s code comments (@dougz and @sublee on github).


References in zbMATH (referenced in 22 articles )

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  1. Pananjady, Ashwin; Mao, Cheng; Muthukumar, Vidya; Wainwright, Martin J.; Courtade, Thomas A.: Worst-case versus average-case design for estimation from partial pairwise comparisons (2020)
  2. Latif, Naveed; Pečarić, Đilda; Pečarić, Josip: Majorizatiuon and Zipf-Mandelbrot law (2018)
  3. Oliveira, I. F. D.; Zehavi, S.; Davidov, O.: Stochastic transitivity: axioms and models (2018)
  4. Oliveira, Ivo F. D.; Ailon, Nir; Davidov, Ori: A new and flexible approach to the analysis of paired comparison data (2018)
  5. Pan, Yuangang; Han, Bo; Tsang, Ivor W.: Stagewise learning for noisy (k)-ary preferences (2018)
  6. Weng, Ruby Chiu-Hsing; Coad, D. Stephen: Real-time Bayesian parameter estimation for item response models (2018)
  7. Latif, Naveed; Pečarić, Đilda; Pečarić, Josip: Majorization, Csiszár divergence and Zipf-Mandelbrot law (2017)
  8. Chen, Xi; Jiao, Kevin; Lin, Qihang: Bayesian decision process for cost-efficient dynamic ranking via crowdsourcing (2016)
  9. Pigozzi, Gabriella; Tsoukiàs, Alexis; Viappiani, Paolo: Preferences in artificial intelligence (2016)
  10. Shah, Nihar B.; Balakrishnan, Sivaraman; Bradley, Joseph; Parekh, Abhay; Ramchandran, Kannan; Wainwright, Martin J.: Estimation from pairwise comparisons: sharp minimax bounds with topology dependence (2016)
  11. Gordon, Andrew D.; Graepel, Thore; Rolland, Nicolas; Russo, Claudio; Borgstrom, Johannes; Guiver, John: Tabular, a schema-driven probabilistic programming language (2014)
  12. Gordon, Andrew D.; Aizatulin, Mihhail; Borgstrom, Johannes; Claret, Guillaume; Graepel, Thore; Nori, Aditya V.; Rajamani, Sriram K.; Russo, Claudio: A model-learner pattern for Bayesian reasoning (2013)
  13. Balakrishnan, Suhrid; Chopra, Sumit: Two of a kind or the ratings game? Adaptive pairwise preferences and latent factor models (2012)
  14. Borgström, Johannes; Gordon, Andrew D.; Greenberg, Michael; Margetson, James; Van Gael, Jurgen: Measure transformer semantics for Bayesian machine learning (2011)
  15. Huang, Jim C.; Frey, Brendan J.: Cumulative distribution networks and the derivative-sum-product algorithm: models and inference for cumulative distribution functions on graphs (2011)
  16. Weng, Ruby C.; Lin, Chih-Jen: A Bayesian approximation method for online ranking (2011)
  17. Pahikkala, Tapio; Waegeman, Willem; Tsivtsivadze, Evgeni; Salakoski, Tapio; de Baets, Bernard: Learning intransitive reciprocal relations with kernel methods (2010)
  18. Beygelzimer, Alina; Langford, John; Ravikumar, Pradeep: Error-correcting tournaments (2009)
  19. Waegeman, Willem; De Baets, Bernard; Boullart, Luc: Kernel-based learning methods for preference aggregation (2009)
  20. Coulom, Rémi: Whole-history rating: A Bayesian rating system for players of time-varying strength (2008)

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