PMTK

PMTK is a collection of Matlab/Octave functions, written by Matt Dunham, Kevin Murphy and various other people. The toolkit is primarily designed to accompany Kevin Murphy’s textbook Machine learning: a probabilistic perspective, but can also be used independently of this book. The goal is to provide a unified conceptual and software framework encompassing machine learning, graphical models, and Bayesian statistics (hence the logo). (Some methods from frequentist statistics, such as cross validation, are also supported.) Since December 2011, the toolbox is in maintenance mode, meaning that bugs will be fixed, but no new features will be added (at least not by Kevin or Matt).


References in zbMATH (referenced in 50 articles )

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  1. Chen, Peixian; Zhang, Nevin L.; Liu, Tengfei; Poon, Leonard K.M.; Chen, Zhourong; Khawar, Farhan: Latent tree models for hierarchical topic detection (2017)
  2. Davies, Vinny; Reeve, Richard; Harvey, William T.; Maree, Francois F.; Husmeier, Dirk: A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution (2017)
  3. Grzegorczyk, Marco; Aderhold, Andrej; Husmeier, Dirk: Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration (2017)
  4. Hejazi, Seyed Amir; Jackson, Kenneth R.: Efficient valuation of SCR via a neural network approach (2017)
  5. Lauri, Mikko; Ropponen, Aino; Ritala, Risto: Meeting a deadline: shortest paths on stochastic directed acyclic graphs with information gathering (2017)
  6. Liu, Manxia; Hommersom, Arjen; van der Heijden, Maarten; Lucas, Peter J.F.: Hybrid time Bayesian networks (2017)
  7. Li, Yuan: Quantum AdaBoost algorithm via cluster state (2017)
  8. Moreira, Catarina; Wichert, Andreas: Exploring the relations between quantum-like Bayesian networks and decision-making tasks with regard to face stimuli (2017)
  9. Öktem, Ozan; Chen, Chong; Domaniç, Nevzat Onur; Ravikumar, Pradeep; Bajaj, Chandrajit: Shape-based image reconstruction using linearized deformations (2017)
  10. Ruiz-Sarmiento, Jose-Raul; Galindo, Cipriano; Gonzalez-Jimenez, Javier: A survey on learning approaches for undirected graphical models. application to scene object recognition (2017)
  11. Tal, Omri; Tran, Tat Dat; Portegies, Jacobus: From typical sequences to typical genotypes (2017)
  12. Wright, James R.; Leyton-Brown, Kevin: Predicting human behavior in unrepeated, simultaneous-move games (2017)
  13. Yao, Tiansheng; Choi, Arthur; Darwiche, Adnan: Learning Bayesian network parameters under equivalence constraints (2017)
  14. Youssef, Abdou: Part-of-math tagging and applications (2017)
  15. Bright, Ido; Lin, Guang; Kutz, J.Nathan: Classification of spatiotemporal data via asynchronous sparse sampling: application to flow around a cylinder (2016)
  16. Chen, Lifei; Ye, Yanfang; Guo, Gongde; Zhu, Jianping: Kernel-based linear classification on categorical data (2016)
  17. Chen, Yutian; Bornn, Luke; de Freitas, Nando; Eskelin, Mareija; Fang, Jing; Welling, Max: Herded Gibbs sampling (2016)
  18. de Campos, Cassio P.; Corani, Giorgio; Scanagatta, Mauro; Cuccu, Marco; Zaffalon, Marco: Learning extended tree augmented naive structures (2016)
  19. Eliazar, Iddo: Harnessing inequality (2016)
  20. Harmandaris, Vagelis; Kalligiannaki, Evangelia; Katsoulakis, Markos; Plecháč, Petr: Path-space variational inference for non-equilibrium coarse-grained systems (2016)

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