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changed BMH_layout to book_layout, made changes

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1 parent 9316205 commit d95f2d8a4560b1ee880f16d51a416d4fb894a583 @CamDavidsonPilon committed Feb 7, 2013
Showing with 59 additions and 56 deletions.
  1. +0 −56 BMH_layout.txt
  2. +59 −0 book_layout.txt
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-# Bayesian Methods for Hackers Layout
-
-\section{ Preamble}
-
-
-\section{ Introduction }
- \
- \subsection{How does bayesian inference differ?}
- \subsection{ PyMC }
-
-
-\section{Further PyMC}
- #flexible about what this section is. Basically it's more intro to the
- syntax of PyMC, with examples + distributions.
-
-\section{ Would you rather lose an arm or a leg? }
- #Introduction to loss functions and point estimation.
- \subsection{ Loss functions for parameters}
- \subsection{ Why we should be interested in expected values }
- \subsubsection{ Optimization with scipy.optimize }
- \subsection{ Point estimate for predictions }
-
-\section{ The greatest theorem never told }
- #This is about the law of large numbers and how a bayesian uses it for estimates.
-
-\section{What should my prior look like?}
- \subsection{Noninformative priors...}
- \subsection{Noninformative priors do not exist}
- \subsection{Good choices of priors }
-
-\section{ Bayesian Networks }
- #I do not know too much about this.
-
-
-\section{More hacking with PyMC}
- #some examples from that PyMC website.
-
-
-\section{ More examples of Bayesian analysis}
-
- \subsection{ For the interested: We already do perform Bayesian inference! }
- #This is about least-squares solution equivilance to minimization of normal errors, similarly Lasso and Elastic Net solutions.
-
-
-
-\section{ Conclusion }
-
-
-\section{Appendix}
- \subsection{A}
- #Chart of distributions and their support
- \subsection{B}
- #Appendix on MCMC
- \section{C}
- #Proofs
-
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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
+

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