pausa forçada para atualizar o meu (fc) sistema :D

Francisco Coelho
1 parent 353a8086
Showing 2 changed files with 13 additions and 4 deletions Show diff stats
text/paper_01/pre-paper.pdf
text/paper_01/pre-paper.tex
@@ -74,6 +74,7 @@ citecolor=blue,
 \newcommand{\probfact}[2]{\ensuremath{#1\!::\!#2}}
 \newcommand{\tcgen}[1]{\ensuremath{\widehat{#1}}}
 \newcommand{\lfrac}[2]{\ensuremath{{#1}/{#2}}}
+\newcommand{\condsymb}[2]{\ensuremath{p{#1}\_{#2}}}
  
 \newcommand{\todo}[1]{{\color{red!50!black}(\emph{#1})}}
 \newcommand{\remark}[2]{\uwave{#1}~{\color{green!40!black}(\emph{#2})}}
@@ -781,7 +782,15 @@ We continue with the specification from Equation \eqref{eq:example.1}.
 %
 \subsection{An example involving Bayesian networks}
  
-As it turns out, our framework is suitable to deal with more sophisticated cases, for example cases involving Bayesian networks. In order to illustrate this, in this section we see how the classical example of the Burglary, Earthquake, Alarm \cite{Judea88} works in our setting. This example is a commonly used example in Bayesian networks because it illustrates reasoning under uncertainty.  The gist of example is given in \cref{Figure_Alarm}. It involves a simple network of events and conditional probabilities. 
+\franc{Cometários:}
+\begin{itemize}
+    \item Há uma macro, $\backslash\text{pr}\{A\}$, para denotar a função de probabilidade, $\pr{A}$ em vez de $P(A)$. Já agora, para a condicional também há um comando, $\backslash\text{given}$: $\pr{A \given B}$.
+    \item E, claro, para factos+probabilidades: $\probfact{p}{a}$.
+    \item A designação dos `pesos' não está consistente: $pj\_a$ e $a\_be$. Fiz uma macro (hehe) para sistematizar isto: \condsymb{a}{bnc}
+\end{itemize}
+
+
+As it turns out, our framework is suitable to deal with more sophisticated cases, \replace{for example}{in particular} cases involving Bayesian networks. In order to illustrate this, in this section we see how the classical example of the Burglary, Earthquake, Alarm \cite{Judea88} works in our setting. This example is a commonly used example in Bayesian networks because it illustrates reasoning under uncertainty.  The gist of example is given in \cref{Figure_Alarm}. It involves a simple network of events and conditional probabilities. 
  
 The events are: Burglary ($B$), Earthquake ($E$), Alarm ($A$), Mary calls ($M$) and John calls ($J$). The initial events $B$ and $E$ are assumed to be independent events that occur with probabilities $P(B)$ and $P(E)$, respectively. There is an alarm system that can be triggered by either of the initial events $B$ and $E$. The probability of the alarm going off is a conditional probability given that $B$ and $E$ have occurred. One denotes these probabilities, as per usual,  by $P(A|B)$, and $P(A|E)$. There are two neighbours, Mary and John who have agreed to call if they hear the alarm. The probability that they do actually call is also a conditional probability denoted by $P(M|A)$ and $P(J|A)$, respectively.
  
@@ -856,7 +865,7 @@ The events are: Burglary ($B$), Earthquake ($E$), Alarm ($A$), Mary calls ($M$) 
 \end{figure}
  
  
-Considering the probabilities given in \cref{Figure_Alarm} we obtain the following specification
+Considering the probabilities given in \cref{Figure_Alarm} we obtain the following spe\-ci\-fi\-ca\-tion
  
 \begin{equation*}
     \begin{aligned}
@@ -871,8 +880,8 @@ For the table giving the probability $P(M|A)$ we obtain the specification:
  
 \begin{equation*}
     \begin{aligned}
-        &\probfact{0.9}{pm\_a},\cr
-        &\probfact{0.05}{pm\_na},\cr
+        \probfact{0.9}{pm\_a}&,\cr
+        \probfact{0.05}{pm\_na}&,\cr
          m & \leftarrow a, pm\_a,\cr
          \neg m & \leftarrow a, \neg pm\_a.
             \end{aligned}
...	...	@@ -74,6 +74,7 @@ citecolor=blue,
74	74	\newcommand{\probfact}[2]{\ensuremath{#1\!::\!#2}}
75	75	\newcommand{\tcgen}[1]{\ensuremath{\widehat{#1}}}
76	76	\newcommand{\lfrac}[2]{\ensuremath{{#1}/{#2}}}
	77	+\newcommand{\condsymb}[2]{\ensuremath{p{#1}\_{#2}}}
77	78
78	79	\newcommand{\todo}[1]{{\color{red!50!black}(\emph{#1})}}
79	80	\newcommand{\remark}[2]{\uwave{#1}~{\color{green!40!black}(\emph{#2})}}
...	...	@@ -781,7 +782,15 @@ We continue with the specification from Equation \eqref{eq:example.1}.
781	782	%
782	783	\subsection{An example involving Bayesian networks}
783	784
784		-As it turns out, our framework is suitable to deal with more sophisticated cases, for example cases involving Bayesian networks. In order to illustrate this, in this section we see how the classical example of the Burglary, Earthquake, Alarm \cite{Judea88} works in our setting. This example is a commonly used example in Bayesian networks because it illustrates reasoning under uncertainty. The gist of example is given in \cref{Figure_Alarm}. It involves a simple network of events and conditional probabilities.
	785	+\franc{Cometários:}
	786	+\begin{itemize}
	787	+ \item Há uma macro, $\backslash\text{pr}\{A\}$, para denotar a função de probabilidade, $\pr{A}$ em vez de $P(A)$. Já agora, para a condicional também há um comando, $\backslash\text{given}$: $\pr{A \given B}$.
	788	+ \item E, claro, para factos+probabilidades: $\probfact{p}{a}$.
	789	+ \item A designação dos `pesos' não está consistente: $pj\_a$ e $a\_be$. Fiz uma macro (hehe) para sistematizar isto: \condsymb{a}{bnc}
	790	+\end{itemize}
	791	+
	792	+
	793	+As it turns out, our framework is suitable to deal with more sophisticated cases, \replace{for example}{in particular} cases involving Bayesian networks. In order to illustrate this, in this section we see how the classical example of the Burglary, Earthquake, Alarm \cite{Judea88} works in our setting. This example is a commonly used example in Bayesian networks because it illustrates reasoning under uncertainty. The gist of example is given in \cref{Figure_Alarm}. It involves a simple network of events and conditional probabilities.
785	794
786	795	The events are: Burglary ($B$), Earthquake ($E$), Alarm ($A$), Mary calls ($M$) and John calls ($J$). The initial events $B$ and $E$ are assumed to be independent events that occur with probabilities $P(B)$ and $P(E)$, respectively. There is an alarm system that can be triggered by either of the initial events $B$ and $E$. The probability of the alarm going off is a conditional probability given that $B$ and $E$ have occurred. One denotes these probabilities, as per usual, by $P(A\|B)$, and $P(A\|E)$. There are two neighbours, Mary and John who have agreed to call if they hear the alarm. The probability that they do actually call is also a conditional probability denoted by $P(M\|A)$ and $P(J\|A)$, respectively.
787	796
...	...	@@ -856,7 +865,7 @@ The events are: Burglary ($B$), Earthquake ($E$), Alarm ($A$), Mary calls ($M$)
856	865	\end{figure}
857	866
858	867
859		-Considering the probabilities given in \cref{Figure_Alarm} we obtain the following specification
	868	+Considering the probabilities given in \cref{Figure_Alarm} we obtain the following spe\-ci\-fi\-ca\-tion
860	869
861	870	\begin{equation*}
862	871	\begin{aligned}
...	...	@@ -871,8 +880,8 @@ For the table giving the probability $P(M\|A)$ we obtain the specification:
871	880
872	881	\begin{equation*}
873	882	\begin{aligned}
874		- &\probfact{0.9}{pm\_a},\cr
875		- &\probfact{0.05}{pm\_na},\cr
	883	+ \probfact{0.9}{pm\_a}&,\cr
	884	+ \probfact{0.05}{pm\_na}&,\cr
876	885	m & \leftarrow a, pm\_a,\cr
877	886	\neg m & \leftarrow a, \neg pm\_a.
878	887	\end{aligned}
...	...