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# Bayesian Methods for Hackers Layout
\section{ Preamble}
\chapter1{ Introduction }
\chapter2{More PyMC / Modeling in PyMC}
#flexible about what this section is. Basically it's more intro to the
syntax of PyMC, with examples + distributions.
\chapter3{ Intro to MCMC and Diagnogstics }
\chapter4{ The greatest theorem never told }
#This is about the law of large numbers and how a bayesian uses it for estimates.
\chapter5{ Would you rather lose an arm or a leg? }
#Introduction to loss functions and point estimation.
>>>>>>>>>
Below is subject to change
\chapter6{What should my prior look like?}
\subsection{Noninformative priors...}
\subsection{Noninformative priors do not exist}
\subsection{Good choices of priors }
\chapter7{ Bayesian Networks }
#I do not know too much about this.
\chapter8{ Gaussian Processes }
# pymc.gp
\chapter9{ Large Scale systems }
#how can we scale PyMC to larger systems/datasets?
\chapter10{More hacking with PyMC}
#some examples from PyMC.
# Potential class?
\section{Appendix}
\subsection{A}
#Chart of distributions and their support
\subsection{B}
#Appendix on MCMC
\section{C}
#Proofs