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Bayes theorem is ( P(\theta | Y) = \frac{P(Y|\theta)P(\theta)}{P(Y)} ).
DisasterModel: A changepoint example, with several variations.
StraightLineFit: A two-parameter linear regression.
WeibullFit: Fitting the parameters of a Weibull distribution.
NormalFit: Fitting the parameters of a normal distribution.
VonMisesFit: Fitting the parameters of a Von Mises distribution.
GelmanBioassay: From section 3.7 of Bayesian Data Analysis by Gelman et al., 2nd ed.
CustomStep: An example of a custom step method.
Mean: Creates a mean function.
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.
Examples of use of John Salvatier's multichain_mcmc
package: https://github.com/jsalvatier/multichain_mcmc/tree/master/multichain_mcmc/examples
Examples for John's gradient_samplers
package: https://github.com/jsalvatier/gradient_samplers/tree/master/gradient_samplers/examples
Abraham Flaxman's blog contains numerous PyMC examples, both for standard statistics and unusual applications, with code snippets: http://healthyalgorithms.wordpress.com/tag/pymc/
Whit Armstrong's comparison of PyMC with other packages for Gelman et al.'s radon dataset: https://github.com/armstrtw/pymc_radon
http://groups.google.com/group/pymc/browse_thread/thread/9dd4620a4a496c8a http://groups.google.com/group/pymc/browse_thread/thread/7c2f42d223be9efa http://groups.google.com/group/pymc/browse_thread/thread/0af5f316a6f3ad25 http://groups.google.com/group/pymc/browse_thread/thread/7a7eae82b64ea041 http://groups.google.com/group/pymc/browse_thread/thread/538f933f3c08809e http://groups.google.com/group/pymc/browse_thread/thread/17c5262b4618c7b5 http://groups.google.com/group/pymc/browse_thread/thread/d754ba79f95b9f78 http://groups.google.com/group/pymc/browse_thread/thread/e2b9c5369569d417 http://groups.google.com/group/pymc/browse_thread/thread/844a1c2de52d72c2 http://groups.google.com/group/pymc/browse_thread/thread/afe91c4d9440d6da http://groups.google.com/group/pymc/browse_thread/thread/a311facec455ef99 http://groups.google.com/group/pymc/browse_thread/thread/3ed5f11b93dc8ddb http://groups.google.com/group/pymc/browse_thread/thread/cc36569d9d002843 http://groups.google.com/group/pymc/browse_thread/thread/e84b6cd07b94b3ef http://groups.google.com/group/pymc/browse_thread/thread/0ba6ca01dbb82a46 http://groups.google.com/group/pymc/browse_thread/thread/2047306d8ea31715 http://groups.google.com/group/pymc/browse_thread/thread/81aa849deae8a1b8 http://groups.google.com/group/pymc/browse_thread/thread/c97ca688bda4ad1b http://groups.google.com/group/pymc/browse_thread/thread/1e9e582113cd6b4b http://groups.google.com/group/pymc/browse_thread/thread/53918b41f8eb4744 http://groups.google.com/group/pymc/browse_thread/thread/4eb664a913ad9011 http://groups.google.com/group/pymc/browse_thread/thread/c23045038a32fb12 http://groups.google.com/group/pymc/browse_thread/thread/4cd684bb4f11ea01 http://groups.google.com/group/pymc/browse_thread/thread/02f1ff5e29343627 http://groups.google.com/group/pymc/browse_thread/thread/e49455c860add294 http://groups.google.com/group/pymc/browse_thread/thread/c6ce37a80edf7f85 http://groups.google.com/group/pymc/browse_thread/thread/87a7d33086f6b4c0 http://groups.google.com/group/pymc/browse_thread/thread/9c1f54daa966c148 http://groups.google.com/group/pymc/browse_thread/thread/f988ce1685951393 http://groups.google.com/group/pymc/browse_thread/thread/41b64c126b62080c http://groups.google.com/group/pymc/browse_thread/thread/fef4582f1ff73077 http://groups.google.com/group/pymc/browse_thread/thread/ec828c63f09a08ae http://groups.google.com/group/pymc/browse_thread/thread/423c06187a482b64 http://groups.google.com/group/pymc/browse_thread/thread/a6092613089e0e70 http://groups.google.com/group/pymc/browse_thread/thread/0001396a059ac969