Page 132 - DCAP310_INTRODUCTION_TO_ARTIFICIAL_INTELLIGENCE_AND_EXPERT_SYSTEMS

Introduction to Artificial Intelligence & Expert Systems Mithilesh Kumar Dubey, Lovely Professional University

Notes Unit 7: Probabilistic Reasoning

CONTENTS
Objectives
Introduction

7.1 Bayesian Probabilistic Inference
7.1.1 Inference
7.1.2 Brief Introduction to the Bayesian Approach

7.1.3 An Alternative to Bayes: The Stanford Certainty Factor Algebra
7.1.4 Graphical Models for Uncertain Reasoning
7.1.5 The Bayesian Belief Network
7.1.6 Inference with a Bayesian Belief Network
7.2 Possible World Representations

7.2.1 Pointing to the Subject
7.2.2 Common Realms of Discourse
7.2.3 Knowledge Representation

7.3 The Dempster – Shafer Theory
7.3.1 Characteristics of D – S
7.3.2 Example of D – S Application
7.3.3 Combining Evidences
7.3.4 Advantages and Disadvantages of D – S Theory

7.4 Heuristic Reasoning Methods
7.4.1 Problems with Current Approaches to Uncertainty
7.5 Summary

7.6 Keywords
7.7 Review Questions
7.8 Further Readings

Objectives

After studying this unit, you will be able to:

Discuss the Bayesian Probabilistic Inference
Describe the Possible World Representations
Explain the Dempster – Shafer Theory
Identify various Heuristic Reasoning Methods

126 LOVELY PROFESSIONAL UNIVERSITY