Tekstuele aanpassingen

diff --git a/report/ass2-1.tex b/report/ass2-1.tex
index 55c8501..462d0c8 100644 (file)
--- a/report/ass2-1.tex
+++ b/report/ass2-1.tex
@@ -12,7 +12,7 @@ Figure~\ref{bnetwork21}
        \includegraphics[scale=0.5]{d1.eps}
 \end{figure}
 
-Days were chosen as unit to model the story. Calculation for the probability of a\textit{Burglar} event happening at some day is then (assuming a gregorian
+Days were chosen as unit to model the story. Calculation of the probability of a\textit{Burglar} event happening at some day is then (assuming a Gregorian
 calendar and leap days):
 $$\frac{1}{365 + 0.25 - 0.01 - 0.0025}=\frac{1}{365.2425}$$
 
@@ -76,7 +76,7 @@ The resultant probability distributions can be found in table ~\ref{probdist}, i
                T & $0.8$ & $0.2$\\
                F & $0.4$ & $0.6$\\
                \hline
-       \end{tabular}
+       \end{tabular} 
        \begin{tabular}{|l|ll|}
                \hline
                & \multicolumn{2}{c|}{Gibbons}\\
@@ -97,17 +97,23 @@ The resultant probability distributions can be found in table ~\ref{probdist}, i
        \end{tabular}
 \end{table}
 
+\textit{If there is a burglar present (which could happen once every ten years), the alarm is known to go off 95\% of the time.} We modelled this by setting the value for Burglar True and $I_2$ True on 0,95. \\
+\textit{There’s a 40\% chance that Watson is joking and the alarm is in fact off.} This is modelled by putting the value for Watson True and Alarm F on 0,4. As Holmes expects Watson to call in 80\% of the time, the value for alarm True and Watson True is set 0,2. Because the rows have to sum to 1, the other values are easily calculated. \\
+\textit{She may not have heard the alarm in 1\% of the cases and is thought to erroneously report an alarm when it is in fact off in 4\% of the cases.} We modelled this by assuming that when Mrs. Gibbons hears the alarm, she calls Holmes. Meaning that the value for Gibbons False and Alarm true is 0,01. As she reports when the alarm is in fact off in 4\% of the cases, the value for Gibbons True and alarm False is 0,04. \\
+
+ 
+
 \section{Implementation}
-This distribution results in the \textit{AILog} code in Listing~\ref{alarm.ail}
+We implemented the distributions in \textit{AILog}, see Listing~\ref{alarm.ail}
 
 \begin{listing}[H]
        \label{alarm.ail}
-       \caption{alarm.ail}
+       \caption{Alarm.ail}
        \inputminted[linenos,fontsize=\footnotesize]{prolog}{./src/alarm.ail}
 \end{listing}
 
 \section{Queries}
-Using the following queries the probabilities or as follows:\\
+Using the following queries the probabilities are as follows:\\
 \begin{enumerate}[a)]
        \item $P(\text{Burglary})=
                0.002737757092501968$
@@ -152,7 +158,7 @@ Answer: P(burglar|Obs)=[0.01179672476662423,0.015584580594335082].
 \end{listing}
 
 \section{Comparison with manual calculation}
-Querying the \textit{Alarm} variable gives the following answer
+Querying the \textit{Alarm} variable gives the following answer:
 \begin{minted}{prolog}
        ailog: predict alarm.
        Answer: P(alarm|Obs)=0.0031469965467367292.
@@ -182,7 +188,7 @@ $P(burglary)\cdot\left(
 A bayesian network representation of the burglary problem with a multitude of
 houses and burglars is possible but would be very big and tedious because all
 the constraints about the burglars must be incorporated in the network.
-The network would look something like in figere~\ref{bnnetworkhouses}
+The network would look something like in figure~\ref{bnnetworkhouses}
 
 \begin{tabular}{|l|l|}
        \hline

diff --git a/report/report.tex b/report/report.tex
index 303e970..e678ba9 100644 (file)
--- a/report/report.tex
+++ b/report/report.tex
@@ -9,6 +9,9 @@
 \usepackage{amssymb}
 \usepackage[hidelinks]{hyperref}
 \usepackage{epstopdf}
+\usepackage{cleveref}
+
+\renewcommand{\arraystretch}{1.3}
 
 \author{
        Lubbers, M.\\