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 28 articles )

Showing results 1 to 20 of 28.
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  1. Grzegorczyk, Marco; Aderhold, Andrej; Husmeier, Dirk: Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration (2017)
  2. Hejazi, Seyed Amir; Jackson, Kenneth R.: Efficient valuation of SCR via a neural network approach (2017)
  3. Li, Yuan: Quantum AdaBoost algorithm via cluster state (2017)
  4. Öktem, Ozan; Chen, Chong; Domaniç, Nevzat Onur; Ravikumar, Pradeep; Bajaj, Chandrajit: Shape-based image reconstruction using linearized deformations (2017)
  5. Bright, Ido; Lin, Guang; Kutz, J.Nathan: Classification of spatiotemporal data via asynchronous sparse sampling: application to flow around a cylinder (2016)
  6. Chen, Yutian; Bornn, Luke; de Freitas, Nando; Eskelin, Mareija; Fang, Jing; Welling, Max: Herded Gibbs sampling (2016)
  7. de Campos, Cassio P.; Corani, Giorgio; Scanagatta, Mauro; Cuccu, Marco; Zaffalon, Marco: Learning extended tree augmented naive structures (2016)
  8. Harmandaris, Vagelis; Kalligiannaki, Evangelia; Katsoulakis, Markos; Plecháč, Petr: Path-space variational inference for non-equilibrium coarse-grained systems (2016)
  9. Hasnat, Md.Abul; Alata, Olivier; Trémeau, Alain: Model-based hierarchical clustering with Bregman divergences and fishers mixture model: application to depth image analysis (2016)
  10. Hejazi, Seyed Amir; Jackson, Kenneth R.: A neural network approach to efficient valuation of large portfolios of variable annuities (2016)
  11. Hernández-Lobato, Daniel; Morales-Mombiela, Pablo; Lopez-Paz, David; Suárez, Alberto: Non-linear causal inference using Gaussianity measures (2016)
  12. Oden, J.Tinsley; Lima, Ernesto A.B.F.; Almeida, Regina C.; Feng, Yusheng; Rylander, Marissa Nichole; Fuentes, David; Faghihi, Danial; Rahman, Mohammad M.; DeWitt, Matthew; Gadde, Manasa; Zhou, J.Cliff: Toward predictive multiscale modeling of vascular tumor growth, computational and experimental oncology for tumor prediction (2016)
  13. Schulam, Peter; Saria, Suchi: Integrative analysis using coupled latent variable models for individualizing prognoses (2016)
  14. Sra, Suvrit; Hosseini, Reshad: Geometric optimization in machine learning (2016)
  15. Taddei, Tommaso; Quarteroni, Alfio; Salsa, Sandro: An offline-online Riemann solver for one-dimensional systems of conservation laws (2016)
  16. Tripathy, Rohit; Bilionis, Ilias; Gonzalez, Marcial: Gaussian processes with built-in dimensionality reduction: applications to high-dimensional uncertainty propagation (2016)
  17. Twomey, Niall; Diethe, Tom; Flach, Peter: On the need for structure modelling in sequence prediction (2016)
  18. Van Haaren, Jan; Van den Broeck, Guy; Meert, Wannes; Davis, Jesse: Lifted generative learning of Markov logic networks (2016)
  19. Zhang, Yan; Wang, Hongzhi; Gao, Hong; Li, Jianzhong: Efficient accuracy evaluation for multi-modal sensed data (2016)
  20. Chen, Feng; Cheng, Qiang; Dong, Jianwu; Yu, Zhaofei; Wang, Guojun; Xu, Wenli: Efficient approximate linear programming for factored MDPs (2015)

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