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diff --git a/graphics/pgf/contrib/tikz-bayesnet/example.tex b/graphics/pgf/contrib/tikz-bayesnet/example.tex new file mode 100644 index 0000000000..19381bd8a1 --- /dev/null +++ b/graphics/pgf/contrib/tikz-bayesnet/example.tex @@ -0,0 +1,158 @@ +% example.tex +% +% Copyright (C) 2010,2011 Laura Dietz +% Copyright (C) 2012 Jaakko Luttinen +% +% This file may be distributed and/or modified +% +% 1. under the LaTeX Project Public License and/or +% 2. under the GNU General Public License. +% +% See the files LICENSE_LPPL and LICENSE_GPL for more details. + +\documentclass[a4paper]{article} + +\usepackage{tikz} +\usetikzlibrary{bayesnet} +%\pgfrealjobname{example} % name of this file + +\title{Graphical Models in Tikz} +\author{Laura Dietz, Jaakko Luttinen} + +\begin{document} + +\maketitle + +TikZ examples for graphical models (Bayesian networks) and directed +factor graphs \cite{Dietz:2010}. + +% A table of node types +\begin{table}[ht] + \caption{Node types} + \begin{center} + \begin{tabular}{llc} + Type & Syntax & Output + \\ + \hline + Latent variable & + \texttt{\textbackslash node[latent]} & + \tikz{ % + \node[latent] {$x$}; % + } + \\ + Observed variable & + \texttt{\textbackslash node[obs]} & + \tikz{ % + \node[obs] {$y$}; % + } + \\ + Deterministic & + \texttt{\textbackslash node[det]} & + \tikz{ % + \node[det] {dot} ; % + } + \\ + Constant & + \texttt{\textbackslash node[const]} & + \tikz{ % + \node[const] {$a$}; % + } + \\ + Factor & + \texttt{\textbackslash node[factor]} & + \tikz{ % + \node[factor] [label=$\mathcal{N}$] {}; % + } + \\ + Factor with nodes & + & + \tikz{ % + \node[obs] (y) {$y$} ; % + \node[latent, left=of y, yshift=0.5cm] (mu) {$\mu$} ; % + \node[latent, left=of y, yshift=-0.5cm] (tau) {$\tau$} ; % + \factor[left=of y] {y-factor} {$\mathcal{N}$} {} {}; + \factoredge {mu,tau} {y-factor} {y} ; % + } + \\ + Plate & + \texttt{\textbackslash plate} & + \tikz{ % + \node[latent] (x) {$x_m$}; % + \plate {} {(x)} {$m \in \mathcal{M}$}; % + } + \\ + Gate & + & + \tikz{ + % Nodes + \node[obs] (k) {$k$}; % + \node[latent, above=2 of k] (l) {$\lambda$}; % + \factor[above=0.8 of k] {k-f} {Multi} {} {}; % + \node[latent, right=of k-f] (paa) {$\phi$}; % + %\node[latent, right=of k-f] (p) {$\phi$}; % + % Connections + \factoredge {paa} {k-f} {k} ; % + % Gate + \gate {} {(k-f)(k-f-caption)} {l} ; % + } + \end{tabular} + \end{center} +\end{table} + + +% Simple Bayesian network +\begin{figure}[ht] + \begin{center} + \begin{tabular}{cc} + \input{model_pca} & + \input{model_pca2} + \end{tabular} + \end{center} + \caption{PCA model as a Bayesian network and a directed factor + graph.} +\end{figure} + +% Latent Dirichlet allocation +\begin{figure}[ht] + \begin{center} + \input{model_lda} + \end{center} + \caption{Latent Dirichlet allocation as directed factor graph.} +\end{figure} + +% Citation influence model +\begin{figure}[ht] + \begin{center} + \input{model_citation_influence} + \end{center} + \caption{Citation influence model with own topics \cite{Dietz:2007} + as directed factor graph.} +\end{figure} + +\clearpage + +\begin{thebibliography}{9} + +\bibitem{Dietz:2010} + Laura Dietz, + \emph{Directed Factor Graph Notation for Generative Models}. + Technical Report. 2010 + +% Laura Dietz, Steffen Bickel, Tobias Scheffer. +% Unsupervised Prediction of Citation Influences. +% In: Proceedings of International Conference on Machine Learning. 2007 +\bibitem{Dietz:2007} + Laura Dietz, Steffen Bickel, Tobias Scheffer, + \emph{Unsupervised Prediction of Citation Influences}. + In: Proceedings of International Conference on Machine + Learning. 2007 + + +\end{thebibliography} + +\end{document} + +%%% Local Variables: +%%% mode: tex-pdf +%%% TeX-master: t +%%% End: |