# Lesson 26 - Module 10 Reasoning with Uncertainty...

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Module 10 Reasoning with Uncertainty - Probabilistic reasoning Version 1 CSE IIT, Kharagpur

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10.1 Instructional Objective The students should understand the role of uncertainty in knowledge representation Students should learn the use of probability theory to represent uncertainty Students should understand the basic of probability theory, including o Probability distributions o Joint probability o Marginal probability o Conditional probability o Independence o Conditional independence Should learn inference mechanisms in probability theory including o Bayes rule o Product rule Should be able to convert natural language statements into probabilistic statements and apply inference rules Students should understand Bayesian networks as a data structure to represent conditional independence Should understand the syntax and semantics of Bayes net Should understand inferencing mechanisms in Bayes net Should understand efficient inferencing techniques like variable ordering Should understand the concept of d-separation Should understand inference mechanism for the special case of polytrees Students should have idea about approximate inference techniques in Bayesian networks At the end of this lesson the student should be able to do the following: Represent a problem in terms of probabilistic statemenst Apply Bayes rule and product rule for inferencing Represent a problem using Bayes net Perform probabilistic inferencing using Bayes net. Version 1 CSE IIT, Kharagpur
Lesson 26 Reasoning with Uncertain information Version 1 CSE IIT, Kharagpur

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10. 2 Probabilistic Reasoning Using logic to represent and reason we can represent knowledge about the world with facts and rules, like the following ones: bird(tweety). fly(X) :- bird(X).
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Lesson 26 - Module 10 Reasoning with Uncertainty...

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