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 63 articles )

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  1. Chen, Eunice Yuh-Jie; Darwiche, Adnan; Choi, Arthur: On pruning with the MDL score (2018)
  2. Mescheder, L. M.; Lorenz, D. A.: An extended Perona-Malik model based on probabilistic models (2018)
  3. Raissi, Maziar; Karniadakis, George Em: Hidden physics models: machine learning of nonlinear partial differential equations (2018)
  4. Raissi, Maziar; Perdikaris, Paris; Karniadakis, George Em: Numerical Gaussian processes for time-dependent and nonlinear partial differential equations (2018)
  5. Aderhold, Andrej; Husmeier, Dirk; Grzegorczyk, Marco: Approximate Bayesian inference in semi-mechanistic models (2017)
  6. Bussas, Matthias; Sawade, Christoph; Kühn, Nicolas; Scheffer, Tobias; Landwehr, Niels: Varying-coefficient models for geospatial transfer learning (2017)
  7. Chen, Jialin; Wang, Lingli; Charbon, Edoardo: A quantum-implementable neural network model (2017)
  8. Chen, Peixian; Zhang, Nevin L.; Liu, Tengfei; Poon, Leonard K.M.; Chen, Zhourong; Khawar, Farhan: Latent tree models for hierarchical topic detection (2017)
  9. Corani, Giorgio; Benavoli, Alessio; Demšar, Janez; Mangili, Francesca; Zaffalon, Marco: Statistical comparison of classifiers through Bayesian hierarchical modelling (2017)
  10. 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)
  11. Epskamp, Sacha; Rhemtulla, Mijke; Borsboom, Denny: Generalized network psychometrics: combining network and latent variable models (2017)
  12. Grzegorczyk, Marco; Aderhold, Andrej; Husmeier, Dirk: Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration (2017)
  13. Hejazi, Seyed Amir; Jackson, Kenneth R.: Efficient valuation of SCR via a neural network approach (2017)
  14. Lauri, Mikko; Ropponen, Aino; Ritala, Risto: Meeting a deadline: shortest paths on stochastic directed acyclic graphs with information gathering (2017)
  15. Liu, Manxia; Hommersom, Arjen; van der Heijden, Maarten; Lucas, Peter J.F.: Hybrid time Bayesian networks (2017)
  16. Li, Yuan: Quantum AdaBoost algorithm via cluster state (2017)
  17. Moreira, Catarina; Wichert, Andreas: Exploring the relations between quantum-like Bayesian networks and decision-making tasks with regard to face stimuli (2017)
  18. Öktem, Ozan; Chen, Chong; Domaniç, Nevzat Onur; Ravikumar, Pradeep; Bajaj, Chandrajit: Shape-based image reconstruction using linearized deformations (2017)
  19. Raissi, Maziar; Perdikaris, Paris; Karniadakis, George Em: Machine learning of linear differential equations using Gaussian processes (2017)
  20. Raissi, Maziar; Perdikaris, Paris; Karniadakis, George Em: Inferring solutions of differential equations using noisy multi-fidelity data (2017)

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