Chris Fonnesbeck edited this page Apr 12, 2016 · 3 revisions

To learn more about PyMC, please refer to the online user's guide.

PyMC in Scientific Research

PyMC is used for Bayesian modeling in a variety of fields. Here is a partial list of publications that cite PyMC in their work.


Basic examples

Intermediate and advanced models

For users familiar with BUGS, here are a few examples that are translated directly from BUGS models; the original code is included in each file as a docstring:

  • Koala Koala sighting model (from Link & Barker 2009)

  • Mt Conditional multinomial mark-recapture model (from Link & Barker 2009)

  • Mt2 Unconditional multinomial mark-recapture model (apparently not possible in BUGS)

  • BayesFactor Simple example of Bayes factor calculation

Gaussian process examples

  • Covariance: Creates a covariance function.

  • Realizations: Draws several realizations.

  • Observations: Observes a mean and covariance, then draws several realizations.

  • BasisCov: Creates a covariance from a basis with normally-distributed coefficients.

  • GPMCMC: Creates a PyMC model containing a Gaussian process, and fits it with MCMC.

  • Non-parametric regression: iPython Notebook of NP regression using GP

Links to external examples

Example-bearing threads on mailing list:

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