Monte-Carlo Experiment

MCEx is an experimental package designed to be allow experimentation with MCMC package design. It's goal is to be simple to use, understand, extend and improve, while still being fast. The hope is that some of the lessons learned in this experimental package lead to improvements in PyMC. People are encouraged to fork MCEx to try out their own designs and improvements as well as make criticisms.

For a tutorial on basic inference, see tutorial.py in the examples folder.

Some design decisions

Design decision	Advantages	Disadvantages
Computational core outsourced to Theano	Simple package code (<400 lines) Efficient Improvements to Theano improve MCEx GPU enabled Automatic Differentiation	Supporting arbitrary stochastics/deterministics more difficult in complex cases
Random variables, distributions, chains, chain history, and model all distinct	Easy to understand design Reflects the mathematical structure Adding new functionality can be done independently from the rest of the package	More verbose
Functional style design	Allows easier exchange of components	Design less similar to Object Oriented design

To Do

• Simplify standard usage.
• Build a GPU example
• Give sample a way of automatically choosing step methods.
• Do some profiling to see why likelihoods are slower in mcex than pymc
• Fix step_methods.gibbs.categorical so that it's faster, currently very slow.
• Implement a potential object which can take incomplete covariances and exploit the conditional independence of nodes to do the whole calculation
• Build examples showcasing different samplers
• Reconsider nphistory design

Possible Changes

• Make HMC and related automatically choose a variance/covariance
• Eliminate model arguments to AddVar, AddData, step methods etc by making it hidden and default
• Eliminate test = argument to AddVar by allowing the user to specify a 'test point'.