From 8622b96de39f06d4f01b713319f7cada4665418e Mon Sep 17 00:00:00 2001
From: Patricio Cubillos <pcubillos@knights.ucf.edu>
Date: Tue, 11 Nov 2014 11:38:23 -0700
Subject: [PATCH] Update README.md

Added Documentation link to the README file.
---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index f19e179..355bbf4 100644
--- a/README.md
+++ b/README.md
@@ -22,6 +22,8 @@
 
 MC<sup>3</sup> provides a set of routines to sample the posterior probability distributions for the model-fitting parameters.  To do so it uses Bayesian Inference through a Markov-chain Monte Carlo algorithm following, either, Differential-Evolution (recomended) or Metropolis Random Walk. It handles Bayesian priors, Gelman-Rubin convergence test, or shared parameters (p<sub>j</sub>=p<sub>i</sub>) over the MCMC iterations.  You can run MC<sup>3</sup> interactively through the python interpreter or from the terminal command line.
 
+Get the extended MC3 documentation [here](doc/MC3_documentation.pdf).
+
 **Modules summary** (project's [source](src/) code):
 * mcmc.py
 > Core module that implements the MCMC algorithm.