X-Git-Url: https://git.martlubbers.net/?a=blobdiff_plain;f=report%2Fass2-1.tex;h=7f2b15f69612d60b83d9595938d4ffb903602011;hb=5c232e0aecd71d51f950bab6006b66a75713f0de;hp=ea4f9aef2ad5f4aeeb9f56574f53771e8b4415ee;hpb=756f0f15aca89e52bebbe4aa3e1a9dc6d72db267;p=ker2014-2.git diff --git a/report/ass2-1.tex b/report/ass2-1.tex index ea4f9ae..7f2b15f 100644 --- a/report/ass2-1.tex +++ b/report/ass2-1.tex @@ -1 +1,110 @@ \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 +represent the causal independence of \textit{Burglar} and \textit{Earthquake} +on \textit{Alarm}. The visual representation of the network is visible in +Figure~\ref{bnetwork21} + +\begin{figure}[H] + \caption{Bayesian network, visual representation} + \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). +$$\frac{1}{365 + 0.25 - 0.01 - 0.0025}=\frac{1}{365.2425}$$ + +This gives the following probability distributions visible in +Table~\ref{probdist} + +\begin{table}[H] + \label{probdist} + \begin{tabular}{|l|ll|} + \hline + & \multicolumn{2}{c|}{Earthquake}\\ + \hline + T & $0.0027$ & $0.9972$ \\ + F & $0.9973$ & $0.0027$\\ + \hline + \end{tabular} + % + \begin{tabular}{|l|ll|} + \hline + & \multicolumn{2}{c|}{Burglar}\\ + \hline + T & $0.0027$ & $0.9973$ \\ + F & $0.9973$ & $0.0027$\\ + \hline + \end{tabular} + + \begin{tabular}{|l|ll|} + \hline + & \multicolumn{2}{c|}{$I_1$}\\ + Earthquake & T & F\\ + \hline + T & $0.2$ & $0.8$\\ + F & $0$ & $1$\\ + \hline + \end{tabular} + \begin{tabular}{|l|ll|} + \hline + & \multicolumn{2}{c|}{$I_2$}\\ + Burglar & T & F\\ + \hline + T & $0.95$ & $0.05$\\ + F & $0$ & $1$\\ + \hline + \end{tabular} + \begin{tabular}{|ll|ll|} + \hline + && \multicolumn{2}{c|}{Alarm}\\ + $I_1$ & $I_2$ & T & F\\ + \hline + T & T & $1$ & $0$\\ + T & F & $1$ & $0$\\ + F & T & $1$ & $0$\\ + F & F & $0$ & $1$\\ + \hline + \end{tabular} + + \begin{tabular}{|l|ll|} + \hline + & \multicolumn{2}{c|}{Watson}\\ + Alarm & T & F\\ + \hline + T & $0.8$ & $0.2$\\ + F & $0.4$ & $0.6$\\ + \hline + \end{tabular} + \begin{tabular}{|l|ll|} + \hline + & \multicolumn{2}{c|}{Gibbons}\\ + Alarm & T & F\\ + \hline + T & $0.99$ & $0.01$\\ + F & $0.04$ & $0.96$\\ + \hline + \end{tabular} + \begin{tabular}{|l|ll|} + \hline + & \multicolumn{2}{c|}{Radio}\\ + Earthquake & T & F\\ + \hline + T & $0.9998$ & $0.0002$\\ + F & $0.0002$ & $0.9998$\\ + \hline + \end{tabular} +\end{table} + +\section{Implementation} +This distribution results in the \textit{AILog} code in Listing~\ref{alarm.ail} + +\begin{listing} + \label{alarm.ail} + \caption{Alarm.ail} + \inputminted[linenos,fontsize=\footnotesize]{prolog}{./src/alarm.ail} +\end{listing}