public:t-622-arti-13-1:lab_5_-_bayesian_networks
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| public:t-622-arti-13-1:lab_5_-_bayesian_networks [2013/03/12 08:58] – created stephan | public: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:// | * http:// | ||
| * http:// | * http:// | ||
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| + | The following laws of probabilities might be helpful: | ||
| + | * < | ||
| + | * Bayes Law: < | ||
| + | * Marginalization/ | ||
| + | * Independence 1: < | ||
| + | * Independence 2: < | ||
| + | |||
| + | All those laws allow arbitrary additional conditions as long as all probabilities have the condition: | ||
| + | * Bayes Law: < | ||
| + | * Marginalization/ | ||
| ==== Problem 1: Smelly Doors ==== | ==== Problem 1: Smelly Doors ==== | ||
| Line 18: | Line 29: | ||
| * it can check whether the room smells bad or not and use that information as an indicator. | * it can check whether the room smells bad or not and use that information as an indicator. | ||
| - | - Design a very simple Bayesian network usable by the NPC to decide when in front of a door, whether he should open it or not. Start by inventing reasonable probabilities for the relation between the contents of the room and its smell. | + | - Design a very simple Bayesian network |
| - How probable is it that the room contains a monster if the air smells bad? | - How probable is it that the room contains a monster if the air smells bad? | ||
| - Argue how, instead of using made-up probabilities, | - Argue how, instead of using made-up probabilities, | ||
| Line 24: | Line 35: | ||
| Hand in: | Hand in: | ||
| - the Bayesian network (e.g., a screenshot and the probability tables for all nodes of the network) | - the Bayesian network (e.g., a screenshot and the probability tables for all nodes of the network) | ||
| + | - answer to question 2 above | ||
| - a few sentences on how the NPC could learn the necessary probabilities | - a few sentences on how the NPC could learn the necessary probabilities | ||
| Line 30: | Line 42: | ||
| {{ : | {{ : | ||
| - | You are a seasoned tomb raider and have spent the last week rummaging through an old pirate cove full of treasure. So far you have opened 100 chests and of those, 50 have in fact contained treasure! Out of these 50, 40 were trapped and you sustained some painful damage from opening them. Out of these 40 trapped chests, 28 were also locked. Now, of the 10 untrapped chests, three were locked. One would think that only chests with treasure would be trapped, but these pirates were truly nasty, they also put traps on chests with no treasure. Of the 50 chests containing no treasure, 20 were trapped! | + | You are a seasoned tomb raider and have spent the last week rummaging through an old pirate cove full of treasure. So far you have opened 100 chests and of those, 50 have in fact contained treasure! Out of these 50, 40 were trapped and you sustained some painful damage from opening them. Out of these 40 trapped chests, 28 were also locked. Now, of the 10 untrapped chests, three were locked. One would think that only chests with treasure would be trapped, but these pirates were truly nasty, they also put traps on chests with no treasure. Of the 50 chests containing no treasure, 20 were trapped! |
| You have now discovered a new chest that you haven' | You have now discovered a new chest that you haven' | ||
/var/www/cadia.ru.is/wiki/data/attic/public/t-622-arti-13-1/lab_5_-_bayesian_networks.1363078703.txt.gz · Last modified: 2024/04/29 13:32 (external edit)