#!/usr/bin/env python
# coding: utf-8

# # Bayesian Methods in Astronomy: Hands-on Statistics

# This is the content for the *Bayesian Methods in Astronomy* workshop, presented at the 227th meeting of the American Astronomical Society.
# The full repository can be found on GitHub: [http://github.com/jakevdp/AAS227Workshop](https://github.com/jakevdp/AAS227Workshop)

# ## Contents
# 
# ### [1. Introduction: Probability and Bayes' Rule](01-Introduction.ipynb)
# 
# ### [2. Simple Bayesian Modeling](02-Simple-Bayesian-Modeling.ipynb)
# - Solutions to exercise and breakout [here](Solutions-02.ipynb)
#    
# ### [3. Bayesian Modeling via Sampling](03-Bayesian-Modeling-With-MCMC.ipynb)
# - Solutions to breakout [here](Solutions-03.ipynb)
# 
# ### [4. Implementing MCMC](04-Implementing-MCMC.ipynb)
# - Solutions to breakout [here](Solutions-04.ipynb)
# 
# ### [5. Application: Radial Velocity Planet Searches](05-Radial-Velocity.ipynb)
# - Solutions to breakout [here](Solutions-05.ipynb)

# ## Requirements
# 
# For this workshop we will be using Python, and in particular *Python version 3.4-3.5*. In addition, we will make use of the following packages, along with their dependencies.
# 
# **Note that familiarity with the following packages is considered a pre-requisite to the workshop. We will not be spending time helping people with setting up or installing these packages.**
# 
# - [NumPy](http://numpy.org) (Numerical Python) for efficient manipulation of array-based data
# - [SciPy](http://scipy.org) (Scientific Python) for optimization and other routines
# - [Pandas](http://pandas.pydata.org) for reading data files into arrays
# - [Matplotlib](http://matplotlib.org) for scientific visualization
# - [IPython notebook](http://ipython.org) as an interactive computing environment
# 
# To install these dependencies, I *highly* recommend using the [miniconda](http://conda.pydata.org/miniconda.html) Python installer *(be sure to download the Python 3.5 version of miniconda)*. Once this is installed, run the following commands in your terminal:
# 
# ```
# $ conda install numpy scipy pandas matplotlib ipython-notebook pip
# ```
# 
# On top of these prerequisites, we will be introducing two relatively lightweight packages designed to enable efficient Bayesian computation in Python.
# They are:
# 
# - [emcee](http://dan.iel.fm/emcee/) for Markov Chain Monte Carlo sampling of Bayesian posteriors
# - [corner.py](https://pypi.python.org/pypi/corner/1.0.0) for visualization of multidimensional posteriors
# 
# These can be installed via the Python Package Index, using the ``pip`` command:
# 
# ```
# $ pip install emcee
# $ pip install corner
# ```

# ## Workshop Material
# 
# All the material for this workshop, as well as the exercises we will work on together, is contained in IPython notebooks within this repository.
# Please clone this repository (if you are familiar with Git) or download the zip file (link is to the right on the main repository page) to download this content; note that I will likely update the material in the days prior to the workshop, so please plan to git-pull or re-download the zip file the morning of the workshop.