From: Mart Lubbers <mart@martlubbers.net>
Date: Tue, 3 Feb 2015 22:13:36 +0000 (+0100)
Subject: ass11ding
X-Git-Url: https://git.martlubbers.net/?a=commitdiff_plain;h=0c11b4aa656c14fb10cd6d9e448033ff34e327bf;p=ker2014-2.git

ass11ding
---

diff --git a/report/ass2-1.tex b/report/ass2-1.tex
index 6a07cda..fff6138 100644
--- a/report/ass2-1.tex
+++ b/report/ass2-1.tex
@@ -186,11 +186,7 @@ Answer: P(burglar|Obs)=[0.01179672476662423,0.015584580594335082].
 	\end{minted}
 \end{listing}
 
-ToDO write down the most probable explanation for the observed evidence
-
 \section{Comparison with manual calculation}
-ToDO: english.
-When we let ailog calculate the probability of alarm. %Wat is Obs hier??
 Querying the \textit{Alarm} variable gives the following answer:
 \begin{minted}{prolog}
 	ailog: predict alarm.
@@ -206,10 +202,13 @@ results in the following answer:\\
 $P(Alarm|burglar, earthquake) =
 P(i_1|burglar)+P(i_2|earthquake)(1-P(i_1|burglar)) =
 0.2*0.0027+0.95*0.0027*(1-0.2*0.0027)=0.00314699654673673$ \\
-TODOOOOOOOOOOO %Ik weet niet of we i_1 en i_2 nog door iets anders vervangen
-% moeten worden.
+
 When you compare the output of AILog and of the variable elimination, you see
-that they are exactly the same. 
+that they are exactly the same. The method with which AILog calculates the
+probability is almost the same but that is mainly because we did not use any
+techniques that are not available in AILog. When we would have done the same
+task with a Bayesian network and the use of Bayes' rule we would have had a
+different method.
 
 \newpage
 \section{Burglary problem with extended information}
@@ -334,5 +333,3 @@ A Bayesian network representation of the extended story is possible, but could
 	F & $0$ & $1$\\
 	\hline
 \end{tabular}
-
-