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 53 articles , 2 standard articles )

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  1. Fisher, Christopher R.; Houpt, Joseph W.; Gunzelmann, Glenn: Fundamental tools for developing likelihood functions within ACT-R (2022)
  2. Pfannschmidt, Karlson; Gupta, Pritha; Haddenhorst, Björn; Hüllermeier, Eyke: Learning context-dependent choice functions (2022)
  3. Thomas Pinder; Daniel Dodd: GPJax: A Gaussian Process Framework in JAX (2022) not zbMATH
  4. Benavoli, Alessio; Azzimonti, Dario; Piga, Dario: A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with skew Gaussian processes (2021)
  5. Frank Schäfer, Mohamed Tarek, Lyndon White, Chris Rackauckas: AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia (2021) arXiv
  6. Goffard, Pierre-Olivier; Laub, Patrick J.: Approximate Bayesian computations to fit and compare insurance loss models (2021)
  7. Karin C. Knudson, Gabe Schoenbach, Amariah Becker: PyEI: A Python package for ecological inference (2021) not zbMATH
  8. Lukas Prediger, Niki Loppi, Samuel Kaski, Antti Honkela: d3p - A Python Package for Differentially-Private Probabilistic Programming (2021) arXiv
  9. Mathieu Besançon, Theodore Papamarkou, David Anthoff, Alex Arslan, Simon Byrne, Dahua Lin, John Pearson: Distributions.jl: Definition and Modeling of Probability Distributions in the JuliaStats Ecosystem (2021) not zbMATH
  10. Perry de Valpine, Sally Paganin, Daniel Turek: compareMCMCs: An R package for studying MCMC efficiency (2021) not zbMATH
  11. Pose, Fernando E.; Bautista, Lucas; Gianmuso, Franco; Redelico, Francisco O.: On the permutation entropy Bayesian estimation (2021)
  12. Pourzanjani, Arya A.; Jiang, Richard M.; Mitchell, Brian; Atzberger, Paul J.; Petzold, Linda R.: Bayesian inference over the Stiefel manifold via the Givens representation (2021)
  13. Andrew R. McCluskey; Tim Snow: uravu: Making Bayesian modelling easy(er) (2020) not zbMATH
  14. Bürkner, Paul-Christian; Gabry, Jonah; Vehtari, Aki: Approximate leave-future-out cross-validation for Bayesian time series models (2020)
  15. Butler, Troy; Wildey, T.; Yen, Tian Yu: Data-consistent inversion for stochastic input-to-output maps (2020)
  16. Fisher, Christopher R.; Houpt, Joseph W.; Gunzelmann, Glenn: Developing memory-based models of ACT-R within a statistical framework (2020)
  17. Gao, Kaifeng; Mei, Gang; Piccialli, Francesco; Cuomo, Salvatore; Tu, Jingzhi; Huo, Zenan: Julia language in machine learning: algorithms, applications, and open issues (2020)
  18. Jauch, Michael; Hoff, Peter D.; Dunson, David B.: Random orthogonal matrices and the Cayley transform (2020)
  19. Linka, Kevin; Peirlinck, Mathias; Kuhl, Ellen: The reproduction number of COVID-19 and its correlation with public health interventions (2020)
  20. Linka, Kevin; Rahman, Proton; Goriely, Alain; Kuhl, Ellen: Is it safe to lift COVID-19 travel bans? The Newfoundland story (2020)

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