ding
authorMart Lubbers <mart@martlubbers.net>
Tue, 27 Jan 2015 10:30:07 +0000 (11:30 +0100)
committerMart Lubbers <mart@martlubbers.net>
Tue, 27 Jan 2015 10:30:07 +0000 (11:30 +0100)
report/ass2-1.tex
report/d2.dot
report/report.tex
report/tabel [new file with mode: 0644]
report/todo.txt

index 69b4850..66fed8f 100644 (file)
@@ -185,11 +185,78 @@ $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 this:
+The network would look something like in figere~\ref{bnnetworkhouses}
+
+\begin{tabular}{|l|l|}
+       \hline
+       Joe &\\
+       \hline
+       T & $\nicefrac{5}{7}$\\
+       F & $\nicefrac{2}{7}$\\
+       \hline
+\end{tabular}
+\begin{tabular}{|l|l|}
+       \hline
+       William &\\
+       \hline
+       T & $\nicefrac{5}{7}$\\
+       F & $\nicefrac{2}{7}$\\
+       \hline
+\end{tabular}
+\begin{tabular}{|l|l|}
+       \hline
+       Jack & \\
+       \hline
+       T & $\nicefrac{5}{7}$\\
+       F & $\nicefrac{2}{7}$\\
+       \hline
+\end{tabular}
+\begin{tabular}{|l|l|}
+       \hline
+       Averall & \\
+       \hline
+       T & $\nicefrac{5}{7}$\\
+       F & $\nicefrac{2}{7}$\\
+       \hline
+\end{tabular}
+
+\begin{tabular}{|llll|ll|}
+       \hline
+       & & & & Burglary &\\
+       Joe & William & Jack & Averall & T & F\\
+       \hline
+       F& F& F& F & $0$ & $1$\\
+       F& F& F& T & $0$ & $1$\\
+       F& F& T& F & $0$ & $1$\\
+       F& F& T& T & $0$ & $1$\\
+       F& T& F& F & $0$ & $1$\\
+       F& T& F& T & $0$ & $1$\\
+       F& T& T& F & $0$ & $1$\\
+       F& T& T& T & $0$ & $1$\\
+       T& F& F& F & $0$ & $1$\\
+       T& F& F& T & $0$ & $1$\\
+       T& F& T& F & $1$ & $0$\\
+       T& F& T& T & $0$ & $1$\\
+       T& T& F& F & $1$ & $0$\\
+       T& T& F& T & $0$ & $1$\\
+       T& T& T& F & $1$ & $0$\\
+       T& T& T& T & $1$ & $0$\\
+       \hline
+\end{tabular}
+\begin{tabular}{|lll|}
+       \hline
+       & Holmes &\\
+       Burglary & T & F\\
+       \hline
+       T & $0.000153$ & $0.999847$\\
+       F & $0$ & $1$\\
+       \hline
+\end{tabular}
+
 
 \begin{figure}[H]
        \caption{Bayesian network of burglars and houses}
-       \label{bnetwork21}
+       \label{bnnetworkhouses}
        \centering
        \includegraphics[scale=0.5]{d2.eps}
 \end{figure}
index a50ec42..3dbdcb3 100644 (file)
@@ -1,3 +1,14 @@
 digraph {
-       n1
+       joe
+       william
+       jack
+       averall
+       burglary
+       holmes
+
+       joe -> burglary
+       william -> burglary
+       jack -> burglary
+       averall -> burglary
+       burglary -> holmes
 }
index ce72e2f..4022a94 100644 (file)
@@ -1,6 +1,7 @@
 \documentclass[twoside,a4paper,titlepage]{report}
 
 \usepackage{graphicx}
+\usepackage{nicefrac}
 \usepackage{minted}
 \usepackage{float}
 \usepackage{enumerate}
diff --git a/report/tabel b/report/tabel
new file mode 100644 (file)
index 0000000..527b98c
--- /dev/null
@@ -0,0 +1,22 @@
+Joe 5/7
+Jack 5/7
+William 5/7
+Averall 5/7
+
+JWJA T F
+0000 0 1
+0001 0 1
+0010 0 1
+0011 0 1
+0100 0 1
+0101 0 1
+0110 0 1
+0111 0 1
+1000 0 1
+1001 0 1
+1010 1 0
+1011 0 1
+1100 1 0
+1101 0 1
+1110 1 0
+1111 1 0
index 7f53fab..e881432 100644 (file)
@@ -1,8 +1,7 @@
 Opdracht 1
 ==========
-5e : Doen
+5e
 1.7: Uitleg verschil en overeenkomsten uitkomst AILog en Variable elimination
-1.8: Dit moet nog helemaal
 
 Opdracht 2
 ==========