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