\chapter{Probabilistic representation and reasoning (and burglars)}
\section{Formal description}
-In our representation of the model we chose to introduce a \textit{Noisy OR} to
+In our representation of the model we introduced a \textit{Noisy OR} to
represent the causal independence of \textit{Burglar} and \textit{Earthquake}
-on \textit{Alarm}. The visual representation of the network is visible in
+on \textit{Alarm}. The representation of the network is displayed in
Figure~\ref{bnetwork21}
\begin{figure}[H]
- \caption{Bayesian network, visual representation}
+ \caption{Bayesian network alarmsystem}
\label{bnetwork21}
\centering
\includegraphics[scale=0.5]{d1.eps}
\end{figure}
-As for the probabilities for \textit{Burglar} and \textit{Earthquake} we chose
-to calculate them using days the unit. Calculation for the probability of a
-\textit{Burglar} event happening at some day is then this(assuming a gregorian
-calendar and leap days).
+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
+calendar and leap days):
$$\frac{1}{365 + 0.25 - 0.01 - 0.0025}=\frac{1}{365.2425}$$
-This gives the following probability distributions visible in
-Table~\ref{probdist}
+The resultant probability distributions can be found in table ~\ref{probdist}, in order to avoid a unclear graph.
\begin{table}[H]
\label{probdist}
\caption{Bayesian network of burglars and houses}
\label{bnnetworkhouses}
\centering
- \includegraphics[scale=0.5]{d2.eps}
+ %\includegraphics[scale=0.5]{d2.eps}
\end{figure}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage[hidelinks]{hyperref}
+\usepackage{epstopdf}
\author{
- Lubbers, M\\
+ Lubbers, M.\\
(s4109503)
\and
- Stam, M van der\\
+ Stam, M. van der\\
(s4250214)
}
\date{\today}
-\title{Knowledge representation and reasoning\\ Assignment 2}
+\title{Knowledge Representation and Reasoning\\ Assignment 2}
\everymath{\displaystyle}