PyMC

PyMC: Bayesian Stochastic Modelling in Python. PyMC is a python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics.


References in zbMATH (referenced in 25 articles , 2 standard articles )

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  1. Jauch, Michael; Hoff, Peter D.; Dunson, David B.: Random orthogonal matrices and the Cayley transform (2020)
  2. Radivojević, Tijana; Akhmatskaya, Elena: Modified Hamiltonian Monte Carlo for Bayesian inference (2020)
  3. Chen, Xi; Hobson, Michael; Das, Saptarshi; Gelderblom, Paul: Improving the efficiency and robustness of nested sampling using posterior repartitioning (2019)
  4. Clerx, M., Robinson, M., Lambert, B., Lei, C.L., Ghosh, S., Mirams, G.R. and Gavaghan, D.J.: Probabilistic Inference on Noisy Time Series (PINTS) (2019) not zbMATH
  5. Cox, Marco; van de Laar, Thijs; de Vries, Bert: A factor graph approach to automated design of Bayesian signal processing algorithms (2019)
  6. Kumar, R.; Colin, C.; Hartikainen, A.; Martin, O. A.: ArviZ a unified library for exploratory analysis of Bayesian models in Python. (2019) not zbMATH
  7. Naik, Pratik; Pandita, Piyush; Aramideh, Soroush; Bilionis, Ilias; Ardekani, Arezoo M.: Bayesian model calibration and optimization of surfactant-polymer flooding (2019)
  8. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  9. Bou-Rabee, Nawaf; Sanz-Serna, J. M.: Geometric integrators and the Hamiltonian Monte Carlo method (2018)
  10. Daniel Emaasit: Pymc-learn: Practical Probabilistic Machine Learning in Python (2018) arXiv
  11. Guillaume Baudart, Martin Hirzel, Kiran Kate, Louis Mandel, Avraham Shinnar: Yaps: Python Frontend to Stan (2018) arXiv
  12. Schreiber, Jacob: pomegranate: fast and flexible probabilistic modeling in Python (2018)
  13. Amen, Saeed: Using Python to analyse financial markets (2017)
  14. Ehrhardt, Matthias (ed.); Günther, Michael (ed.); ter Maten, E. Jan W. (ed.): Novel methods in computational finance (2017)
  15. Wright, James R.; Leyton-Brown, Kevin: Predicting human behavior in unrepeated, simultaneous-move games (2017)
  16. Barany, Vince; ten Cate, Balder; Kimelfeld, Benny; Olteanu, Dan; Vagena, Zografoula: Declarative probabilistic programming with Datalog (2016)
  17. Hernández-Lobato, José Miguel; Gelbart, Michael A.; Adams, Ryan P.; Hoffman, Matthew W.; Ghahramani, Zoubin: A general framework for constrained Bayesian optimization using information-based search (2016)
  18. Luttinen, Jaakko: BayesPy: variational Bayesian inference in Python (2016)
  19. Parish, Eric J.; Duraisamy, Karthik: A paradigm for data-driven predictive modeling using field inversion and machine learning (2016)
  20. John Salvatier, Thomas Wiecki, Christopher Fonnesbeck: Probabilistic Programming in Python using PyMC (2015) arXiv

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