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Commit cc7f651d authored by Manuel Baum's avatar Manuel Baum
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Meinen Teil in die Präsi eingefügt

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doc/presentation/input/manu.tex
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\section{manu}
\ No newline at end of file
\section{Approximate Inference}
\subsection{Motivation}
\begin{frame}
\Large\textbf{Why use approximate Inference?}
\begin{itemize}
\item Possible to include non-linear dependencies of/on real-valued variables
\item Only local computations $\rightarrow$ size of net rather unimportant
\item Possible to trade off computation time for accuracy
\end{itemize}
\end{frame}
\subsection{Structure}
\begin{frame}
\Large\textbf{Whats the stucture?}
\begin{block}{There are two central classes for computation:}
\begin{itemize}
\item MCMC - Interface for usage, final computations
\item MarkovChainSampler - Constructs markov chains
\end{itemize}
\end{block}
\begin{block}{Other classes:}
\begin{itemize}
\item Evidence
\item ContinuousNode (and densities: Gauss, Exponential, Beta)
\end{itemize}
\end{block}
\end{frame}
\begin{frame}
\Large\textbf{MarkovChainSampler}
generates a markov-chain given some evidence when chain has converged
\begin{block}{Parameters}
\begin{itemize}
\item transition\_model: Gibbs or MetropolisHastings
\item convergence\_test: Test for convergence
\item time\_steps: Maximum length of chain
\item evidence: Different kinds of evidence for a subset of nodes
\end{itemize}
\end{block}
\end{frame}
\begin{frame}
\Large\textbf{MCMC-class}
This encapsules MarkovChainSampler
\begin{block}{Possible requests:}
\begin{itemize}
\item def calculate\_PriorMarginal(self, variables, AssumedDensity):
\item def calculate\_MAP(self, variables, evidence, AssumedDensity):
\item def calculate\_PosteriorMarginal(self, variables, evidence, AssumedDensity):
\item def calculate\_PoE(self, evidence):
\end{itemize}
\end{block}
\end{frame}
\begin{frame}
\Large\textbf{Real-valued variables}
\begin{block}{Which densities are supported?}
\begin{itemize}
\item Gaussian
\item Beta
\item Exponential
\end{itemize}
\end{block}
\end{frame}
\subsection{Literature}
\Large\textbf{Literature}
\textsc{Daphne Koller and Nir Friedman} \\ Probabilistic Graphical Models: Principles and Techniques
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\section{Approximate Inference}
\subsection{Motivation}
\begin{frame}
\Large\textbf{Why use approximate Inference?}
\begin{itemize}
\item Possible to include non-linear dependencies of/on real-valued variables
\item Only local computations $\rightarrow$ size of net rather unimportant
\item Possible to trade off computation time for accuracy
\end{itemize}
\end{frame}
\subsection{Structure}
\begin{frame}
\Large\textbf{Whats the stucture?}
\begin{block}{There are two central classes for computation:}
\begin{itemize}
\item MCMC - Interface for usage, final computations
\item MarkovChainSampler - Constructs markov chains
\end{itemize}
\end{block}
\begin{block}{Other classes:}
\begin{itemize}
\item Evidence
\item ContinuousNode (and densities: Gauss, Exponential, Beta)
\end{itemize}
\end{block}
\end{frame}
\begin{frame}
\Large\textbf{MarkovChainSampler}
generates a markov-chain given some evidence when chain has converged
\begin{block}{Parameters}
\begin{itemize}
\item transition\_model: Gibbs or MetropolisHastings
\item convergence\_test: Test for convergence
\item time\_steps: Maximum length of chain
\item evidence: Different kinds of evidence for a subset of nodes
\end{itemize}
\end{block}
\end{frame}
\begin{frame}
\Large\textbf{MCMC}
Danach in MCMC klasse ergebnisse berechnen
\end{frame}
\begin{frame}
\Large\textbf{Real-valued variables}
Which kinds of nodes are there?
Gaussian, Beta, Exponential
\end{frame}
\subsection{Literature}
asdf
\ No newline at end of file
doc/presentation/primo.aux
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doc/presentation/primo.out
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\BOOKMARK [2][]{Outline0.1}{Introduction}{}% 1
\BOOKMARK [2][]{Outline0.2}{Dynamic Bayesian Networks}{}% 2
\BOOKMARK [3][]{Outline0.2.1.4}{Definition}{Outline0.2}% 3
\BOOKMARK [3][]{Outline0.2.2.5}{Example}{Outline0.2}% 4
\BOOKMARK [3][]{Outline0.2.3.6}{Inference}{Outline0.2}% 5
\BOOKMARK [3][]{Outline0.2.4.11}{Structure}{Outline0.2}% 6
\BOOKMARK [3][]{Outline0.2.5.13}{Literature}{Outline0.2}% 7
\BOOKMARK [2][]{Outline0.3}{Factor Trees}{}% 8
\BOOKMARK [3][]{Outline0.3.1.14}{Task Description}{Outline0.3}% 9
\BOOKMARK [3][]{Outline0.3.2.15}{Factor Elimination}{Outline0.3}% 10
\BOOKMARK [3][]{Outline0.3.3.15}{Elimination Trees}{Outline0.3}% 11
\BOOKMARK [3][]{Outline0.3.4.15}{Building Strategies}{Outline0.3}% 12
\BOOKMARK [3][]{Outline0.3.5.15}{Literature}{Outline0.3}% 13
\BOOKMARK [2][]{Outline0.4}{manu}{}% 14
\BOOKMARK [2][]{Outline0.5}{Bayesian Decision Network}{}% 15
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\BOOKMARK [2][]{Outline0.1}{Introduction}{}
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\BOOKMARK [3][]{Outline0.5.5.29}{Literature}{Outline0.5}
doc/presentation/primo.pdf
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doc/presentation/primo.toc
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......@@ -13,10 +13,13 @@
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examples/CalculateMarginals.py
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......@@ -80,6 +80,7 @@ print "Posterior of burglary : " + str(fe.calculate_PosteriorMarginal([burglary]
factorTreeFactory = FactorTreeFactory()
factorTree = factorTreeFactory.create_greedy_factortree(bn)
factorTree.draw()
#For large nets or much queries the factorTree is the most efficient way.
#The first query is expensive in calculation but all following queries are very
......
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