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public:t-622-arti-13-1:lab_5_-_bayesian_networks [2013/03/12 09:18] – [Problem 1: Smelly Doors] stephanpublic:t-622-arti-13-1:lab_5_-_bayesian_networks [2024/04/29 13:33] (current) – external edit 127.0.0.1
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   * http://www.aispace.org/bayes/bayes.jnlp (using Java Web Start)   * http://www.aispace.org/bayes/bayes.jnlp (using Java Web Start)
   * http://www.cs.cmu.edu/~javabayes/ (a slightly older Java applet)   * http://www.cs.cmu.edu/~javabayes/ (a slightly older Java applet)
 +
 +The following laws of probabilities might be helpful:
 +  * <latex>$P(\neg A) = 1 - P(A)$</latex>
 +  * Bayes Law: <latex>$P(A \land B) = P(A|B) * P(B)$</latex>
 +  * Marginalization/Summing out: <latex>$P(A) = \sum_b P(A \land b) = P(A \land B) + P(A \land \neg B)$</latex>
 +  * Independence 1: <latex>$P(A|B,C) = P(A|C)$</latex>, if <latex>$A$</latex> and <latex>$B$</latex> are independent given <latex>$C$</latex>.
 +  * Independence 2: <latex>$P(A \land B|C) = P(A|C) * P(B|C)$</latex>, if <latex>$A$</latex> and <latex>$B$</latex> are independent given <latex>$C$</latex>. (This follows from Independence 1 and Bayes Law.)
 +
 +All those laws allow arbitrary additional conditions as long as all probabilities have the condition:
 +  * Bayes Law: <latex>$P(A \land B|C) = P(A|B,C) * P(B|C)$</latex>
 +  * Marginalization/Summing out: <latex>$P(A|C) = \sum_b P(A \land b|C) = P(A \land B|C) + P(A \land \neg B|C)$</latex>
  
 ==== Problem 1: Smelly Doors ==== ==== Problem 1: Smelly Doors ====
/var/www/cadia.ru.is/wiki/data/attic/public/t-622-arti-13-1/lab_5_-_bayesian_networks.1363079888.txt.gz · Last modified: 2024/04/29 13:32 (external edit)

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