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 38 articles , 2 standard articles )

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  1. Lukas Prediger, Niki Loppi, Samuel Kaski, Antti Honkela: d3p - A Python Package for Differentially-Private Probabilistic Programming (2021) arXiv
  2. Pose, Fernando E.; Bautista, Lucas; Gianmuso, Franco; Redelico, Francisco O.: On the permutation entropy Bayesian estimation (2021)
  3. Andrew R. McCluskey; Tim Snow: uravu: Making Bayesian modelling easy(er) (2020) not zbMATH
  4. Butler, Troy; Wildey, T.; Yen, Tian Yu: Data-consistent inversion for stochastic input-to-output maps (2020)
  5. Fisher, Christopher R.; Houpt, Joseph W.; Gunzelmann, Glenn: Developing memory-based models of ACT-R within a statistical framework (2020)
  6. Gao, Kaifeng; Mei, Gang; Piccialli, Francesco; Cuomo, Salvatore; Tu, Jingzhi; Huo, Zenan: Julia language in machine learning: algorithms, applications, and open issues (2020)
  7. Jauch, Michael; Hoff, Peter D.; Dunson, David B.: Random orthogonal matrices and the Cayley transform (2020)
  8. Linka, Kevin; Peirlinck, Mathias; Kuhl, Ellen: The reproduction number of COVID-19 and its correlation with public health interventions (2020)
  9. Linka, Kevin; Rahman, Proton; Goriely, Alain; Kuhl, Ellen: Is it safe to lift COVID-19 travel bans? The Newfoundland story (2020)
  10. Martin Nielsen, Guy Davies, Oliver Hall, et al.: PBjam: A Python package for automating asteroseismology of solar-like oscillators (2020) arXiv
  11. Oliver Schulz, Frederik Beaujean, Allen Caldwell, Cornelius Grunwald, Vasyl Hafych, Kevin Kröninger, Salvatore La Cagnina, Lars Röhrig, Lolian Shtembari: BAT.jl - A Julia-based tool for Bayesian inference (2020) arXiv
  12. Peirlinck, Mathias; Linka, Kevin; Sahli Costabal, Francisco; Bhattacharya, Jay; Bendavid, Eran; Ioannidis, John P. A.; Kuhl, Ellen: Visualizing the invisible: the effect of asymptomatic transmission on the outbreak dynamics of COVID-19 (2020)
  13. Radivojević, Tijana; Akhmatskaya, Elena: Modified Hamiltonian Monte Carlo for Bayesian inference (2020)
  14. René, Alexandre; Longtin, André; Macke, Jakob H.: Inference of a mesoscopic population model from population spike trains (2020)
  15. Tomás Capretto, Camen Piho, Ravin Kumar, Jacob Westfall, Tal Yarkoni, Osvaldo A. Martin: Bambi: A simple interface for fitting Bayesian linear models in Python (2020) arXiv
  16. Chen, Xi; Hobson, Michael; Das, Saptarshi; Gelderblom, Paul: Improving the efficiency and robustness of nested sampling using posterior repartitioning (2019)
  17. 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
  18. Kumar, R.; Colin, C.; Hartikainen, A.; Martin, O. A.: ArviZ a unified library for exploratory analysis of Bayesian models in Python. (2019) not zbMATH
  19. Naik, Pratik; Pandita, Piyush; Aramideh, Soroush; Bilionis, Ilias; Ardekani, Arezoo M.: Bayesian model calibration and optimization of surfactant-polymer flooding (2019)
  20. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)

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