Small updates to manual
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@ -38,7 +38,7 @@ CRACS \& INESC TEC, Faculty of Sciences, University of Porto
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\thispagestyle{empty}
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\thispagestyle{empty}
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\vspace{5cm}
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\vspace{5cm}
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\begin{center}
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\begin{center}
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\large Last revision: January 18, 2013
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\large Last revision: April 12, 2013
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\end{center}
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\end{center}
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\newpage
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\newpage
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@ -87,7 +87,7 @@ A first-order probabilistic graphical model is described using parametric factor
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$$Type~~F~~;~~Phi~~;~~C.$$
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$$Type~~F~~;~~Phi~~;~~C.$$
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, where
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Where,
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\begin{itemize}
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\begin{itemize}
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\item $Type$ refers the type of network over which the parfactor is defined. It can be \texttt{bayes} for directed networks, or \texttt{markov} for undirected ones.
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\item $Type$ refers the type of network over which the parfactor is defined. It can be \texttt{bayes} for directed networks, or \texttt{markov} for undirected ones.
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@ -489,9 +489,9 @@ The options that are available with the \texttt{set\_pfl\_flag/2} predicate can
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%------------------------------------------------------------------------------
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%------------------------------------------------------------------------------
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%------------------------------------------------------------------------------
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%------------------------------------------------------------------------------
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%------------------------------------------------------------------------------
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%------------------------------------------------------------------------------
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\section{Further Information}
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\section{Papers}
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Please check the paper \textit{Evaluating Inference Algorithms for the Prolog Factor Language} for further information.
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\begin{itemize}
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\item \textit{Evaluating Inference Algorithms for the Prolog Factor Language}.
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Any question? Don't hesitate to contact us!
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\end{itemize}
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\end{document}
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\end{document}
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