# 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