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Graphical Models - Artificial Intelligence - Lecture Slides

Slides, Artificial Intelligence

Post: April 29th, 2013
Description
Some concept of Artificial Intelligence are Agents and Problem Solving, Autonomy, Programs, Classical and Modern Planning, First-Order Logic, Resolution Theorem Proving, Search Strategies, Structure Learning. Main points of this lecture are: Graphical Models, Conditional Independence, Bayesian Networks, Acyclic Directed Graph, Vertices, Edges, Markov Condition, Resolving Conflict, Paraconsistent Reasoning, Propagation
Some concept of Artificial Intelligence are Agents and Problem Solving, Autonomy, Programs, Classical and Modern Planning, First-Order Logic, Resolution Theorem Proving, Search Strategies, Structure Learning. Main points of this lecture are: Graphical Models, Conditional Independence, Bayesian Networks, Acyclic Directed Graph, Vertices, Edges, Markov Condition, Resolving Conflict, Paraconsistent Reasoning, Propagation
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Contents
Lecture 31 of 41 Introduction to Graphical Models Part 2 of 2 Docsity.com Graphical Models Overview [1]: Bayesian Networks • Conditional Independence – X is conditionally independent (CI) from Y given Z (sometimes written X  Y | Z) iff P(X | Y, Z) = P(X | Z) for all values of X, Y, and Z – Example: P(Thunder | Rain, Lightning) = P(Thunder | Lightning)  T  R | L • Bayesian (Belief) Network – Acyclic directed graph model B = (V, E, ) representing CI assertions over  – Vertices (nodes) V: denote events (each a random variable) – Edges (arcs, links) E: denote conditional dependencies • • Markov Condition for BBNs (Chain Rule): P X , X ,  , X   P X | parentsX  i 1 2 n i i 1 Example BBN Exposure-To-Toxins Serum Calcium Age X1 X3 Cancer X5 Gender X2 X4 X7 Lung  Tumor   Smoking       X6 n Descendant s Non Descendant s Parents P(20s, Female, Low, Non-Smoker, No-Cancer, ..

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