Bayesian Cognitive Modeling in PyMC3

PyMC3 port of Lee and Wagenmakers' Bayesian Cognitive Modeling - A Practical Course

All the codes are in jupyter notebook with the model explain in distributions (as in the book). Background information of the models please consult the book. You can also compare the result with the original code associated with the book (WinBUGS and JAGS; Stan)

All the codes are currently tested under PyMC3 v3.2 with theano 0.10.0.dev

In [1]:
# Python Environment and library version
%load_ext watermark
%watermark -v -m -p pymc3,theano,scipy,numpy,pandas,matplotlib,seaborn
CPython 3.5.1
IPython 6.2.1

pymc3 3.2
theano 0.10.0dev1.dev-d51233517debf3a1231a33058d65e8f969db923d
scipy 0.19.1
numpy 1.12.0
pandas 0.20.2
matplotlib 2.0.2
seaborn 0.7.0

compiler   : GCC 4.2.1 (Apple Inc. build 5666) (dot 3)
system     : Darwin
release    : 16.7.0
machine    : x86_64
processor  : i386
CPU cores  : 4
interpreter: 64bit