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 211 articles )

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  1. Fu, Jinlong; Xiao, Dunhui; Li, Dongfeng; Thomas, Hywel R.; Li, Chenfeng: Stochastic reconstruction of 3D microstructures from 2D cross-sectional images using machine learning-based characterization (2022)
  2. Gahm, Christian; Uzunoglu, Aykut; Wahl, Stefan; Ganschinietz, Chantal; Tuma, Axel: Applying machine learning for the anticipation of complex nesting solutions in hierarchical production planning (2022)
  3. Höppner, Sebastiaan; Baesens, Bart; Verbeke, Wouter; Verdonck, Tim: Instance-dependent cost-sensitive learning for detecting transfer fraud (2022)
  4. Pan, Yating; Fei, Yu; Ni, Mingming; Nummi, Tapio; Pan, Jianxin: Growth curve mixture models with unknown covariance structures (2022)
  5. Sembach, Lena; Burgard, Jan Pablo; Schulz, Volker: A Riemannian Newton trust-region method for fitting Gaussian mixture models (2022)
  6. Bengio, Yoshua; Lodi, Andrea; Prouvost, Antoine: Machine learning for combinatorial optimization: a methodological tour d’horizon (2021)
  7. Bhattacharya, Kaushik; Hosseini, Bamdad; Kovachki, Nikola B.; Stuart, Andrew M.: Model reduction and neural networks for parametric PDEs (2021)
  8. Birmpa, Panagiota; Katsoulakis, Markos A.: Uncertainty quantification for Markov random fields (2021)
  9. Castelletti, Federico; Peluso, Stefano: Equivalence class selection of categorical graphical models (2021)
  10. Chakraborty, S.; Adhikari, S.; Ganguli, R.: The role of surrogate models in the development of digital twins of dynamic systems (2021)
  11. Chataigner, Marc; Cousin, Areski; Crépey, Stéphane; Dixon, Matthew; Gueye, Djibril: Short communication: Beyond surrogate modeling: learning the local volatility via shape constraints (2021)
  12. Chen, Yangang; Wan, Justin W. L.: Deep neural network framework based on backward stochastic differential equations for pricing and hedging American options in high dimensions (2021)
  13. Cozman, Fabio Gagliardi; Munhoz, Hugo Neri: Some thoughts on knowledge-enhanced machine learning (2021)
  14. Evans, Richard; Hernández-Orallo, José; Welbl, Johannes; Kohli, Pushmeet; Sergot, Marek: Making sense of sensory input (2021)
  15. Girolami, Mark; Febrianto, Eky; Yin, Ge; Cirak, Fehmi: The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions (2021)
  16. Hütter, Jan-Christian; Rigollet, Philippe: Minimax estimation of smooth optimal transport maps (2021)
  17. Kadane, Joseph B.: Partitioning some multivariate distributions (2021)
  18. Keller, Rachael T.; Du, Qiang: Discovery of dynamics using linear multistep methods (2021)
  19. Kojevnikov, Denis; Marmer, Vadim; Song, Kyungchul: Limit theorems for network dependent random variables (2021)
  20. Liu, Jiapeng; Kadziński, Miłosz; Liao, Xiuwu; Mao, Xiaoxin: Data-driven preference learning methods for value-driven multiple criteria sorting with interacting criteria (2021)

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