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PyMC: Bayesian Stochastic Modelling in Python

Author

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  • Patil, Anand
  • Huard, David
  • Fonnesbeck, Christopher J.

Abstract

This user guide describes a Python package, PyMC, that allows users to efficiently code a probabilistic model and draw samples from its posterior distribution using Markov chain Monte Carlo techniques.

Suggested Citation

  • Patil, Anand & Huard, David & Fonnesbeck, Christopher J., 2010. "PyMC: Bayesian Stochastic Modelling in Python," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i04).
    • Handle: RePEc:jss:jstsof:v:035:i04
    DOI: http://hdl.handle.net/10.18637/jss.v035.i04
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    Cited by:

    1. Wright, James R. & Leyton-Brown, Kevin, 2017. "Predicting human behavior in unrepeated, simultaneous-move games," Games and Economic Behavior, Elsevier, vol. 106(C), pages 16-37.
    2. Bruzzone, Octavio A. & Logarzo, Guillermo A. & Aguirre, María B. & Virla, Eduardo G., 2018. "Intra-host interspecific larval parasitoid competition solved using modelling and bayesian statistics," Ecological Modelling, Elsevier, vol. 385(C), pages 114-123.
    3. Liu, Xiaoqi & Lee, Seungjae & Bilionis, Ilias & Karava, Panagiota & Joe, Jaewan & Sadeghi, Seyed Amir, 2021. "A user-interactive system for smart thermal environment control in office buildings," Applied Energy, Elsevier, vol. 298(C).
    4. Julian C Evans & Colin J Torney & Stephen C Votier & Sasha R X Dall, 2019. "Social information use and collective foraging in a pursuit diving seabird," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-15, September.
    5. Andrew Gelman & Daniel Lee & Jiqiang Guo, 2015. "Stan," Journal of Educational and Behavioral Statistics, , vol. 40(5), pages 530-543, October.
    6. Trung Hai Nguyen & Ariën S Rustenburg & Stefan G Krimmer & Hexi Zhang & John D Clark & Paul A Novick & Kim Branson & Vijay S Pande & John D Chodera & David D L Minh, 2018. "Bayesian analysis of isothermal titration calorimetry for binding thermodynamics," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-26, September.
    7. Won-Mo Jung & Ye-Seul Lee & Christian Wallraven & Younbyoung Chae, 2017. "Bayesian prediction of placebo analgesia in an instrumental learning model," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-11, February.
    8. Weijie Liu & Yan Shen & Lijuan Shen, 2022. "Degradation Modeling for Lithium-Ion Batteries with an Exponential Jump-Diffusion Model," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    9. Tobias Houska & Philipp Kraft & Alejandro Chamorro-Chavez & Lutz Breuer, 2015. "SPOTting Model Parameters Using a Ready-Made Python Package," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-22, December.
    10. Claudia Sala & Enrico Giampieri & Silvia Vitali & Paolo Garagnani & Daniel Remondini & Armando Bazzani & Claudio Franceschi & Gastone C Castellani, 2020. "Gut microbiota ecology: Biodiversity estimated from hybrid neutral-niche model increases with health status and aging," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-23, October.
    11. Alsudais, Abdulkareem, 2021. "In-code citation practices in open research software libraries," Journal of Informetrics, Elsevier, vol. 15(2).
    12. Rungskunroch, Panrawee & Jack, Anson & Kaewunruen, Sakdirat, 2021. "Benchmarking on railway safety performance using Bayesian inference, decision tree and petri-net techniques based on long-term accidental data sets," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    13. Chakraborty, Shantanu & Okabe, Toshiya, 2016. "Robust energy storage scheduling for imbalance reduction of strategically formed energy balancing groups," Energy, Elsevier, vol. 114(C), pages 405-417.
    14. Clithero, John A., 2018. "Improving out-of-sample predictions using response times and a model of the decision process," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 344-375.

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