jsalvatier

#!/bin/sh
curl https://bootstrap.pypa.io/get-pip.py > get-pip.py
sudo python get-pip.py
sudo python -m pip install git+https://github.com/pymc-devs/pymc3
sudo python -m pip install “ipython[notebook]”

git clone https://github.com/pymc-devs/pymc3.git
cd pymc3/pymc3/examples 
python -m IPython notebook

jsalvatier

class CumSum(theano.Op):
    """
    This class is a wrapper for numpy cumsum function
    """
    def __eq__(self, other):
        return (type(self) == type(other))

    def __str__(self):
        return self.__class__.__name__

jsalvatier

PyMC 3

PyMC is a python module for Bayesian statistical modeling and model fitting which focuses on advanced Markov chain Monte Carlo fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.

Features

• Intuitive model specification syntax, for example, x ~ N(0,1) translates to x = Normal(0,1)
• Powerful sampling algorithms such as Hamiltonian Monte Carlo

jsalvatier

PyMC 3

PyMC grew out of, MCEx, 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. This branch is still experimental so people are encouraged to try out their own designs and improvements as well as make criticisms.