'Classical probability' presentation slideshows

Chapter 5: Inexact Reasoning

Chapter 5: Inexact Reasoning. Objectives. Explore the sources of uncertainty in rules Analyze some methods for dealing with uncertainty Learn about the Dempster-Shafer theory Learn about the theory of uncertainty based on fuzzy logic Discuss some commercial applications of fuzzy logic.

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Chapter 4

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Chapter 4

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Bayesian Statistics and Belief Networks

Bayesian Statistics and Belief Networks. Overview. Book: Ch 13,14 Refresher on Probability Bayesian classifiers Belief Networks / Bayesian Networks. Why Should We Care?. Theoretical framework for machine learning, classification, knowledge representation, analysis

665 views • 36 slides

Gradational Accuracy and non-classical logic:

Gradational Accuracy and non-classical logic:. a graphical guide. J. R. G. Williams, University of Leeds. Worlds and probability space.

472 views • 36 slides

Quantum Harmonic Oscillator

Quantum Harmonic Oscillator. Quantum Harmonic Oscillator. 2006 Quantum Mechanics. Prof. Y. F. Chen. Quantum Harmonic Oscillator. Quantum Harmonic Oscillator. 1D S.H.O. ? linear restoring force , k is the force constant & parabolic potential

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Chapter 5. A Survey Of Probability Concepts

Chapter 5. A Survey Of Probability Concepts. What Is A Probability?. The weather forecaster announces that there is a 70 percent chance of rain for Super Bowl Sunday. Probability:

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Probability

Probability. Unit 3. Probability. When weather forecasters say that there is a 90% chance of rain or a physician says there is a 35% chance for a successful surgery, they are stating the likelihood, or probability, that a specific event will occur.

647 views • 33 slides

Chapter 3

Chapter 3. Probability. Chapter Outline. 3.1 Basic Concepts of Probability 3.2 Conditional Probability and the Multiplication Rule 3.3 The Addition Rule 3.4 Additional Topics in Probability and Counting. Section 3.1. Basic Concepts of Probability. Section 3.1 Objectives.

820 views • 33 slides

Chapter 3

Chapter 3. Probability. Chapter Outline. 3.1 Basic Concepts of Probability 3.2 Conditional Probability and the Multiplication Rule 3.3 The Addition Rule 3.4 Additional Topics in Probability and Counting. Section 3.1. Basic Concepts of Probability. Section 3.1 Objectives.

452 views • 33 slides

Learning with Bayesian Networks

Learning with Bayesian Networks. David Heckerman Presented by Colin Rickert. Introduction to Bayesian Networks. Bayesian networks represent an advanced form of general Bayesian probability

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Probability

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3 Types of Probabilities

3 Types of Probabilities. 3 Types of Probabilities. 1. Classical Probability. Uses sample spaces to determine the numerical probability that an event will occur. n(E) n(S). P(E) = . 3 Types of Probabilities. 2. Empirical Probability.

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Chapter 3

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Chapter 3

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Lecture 6

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Chapter 9 Probability

Chapter 9 Probability. James A. Van Slyke Azusa Pacific University. Probability . a priori probability Classical probability That which can be deduced from reason alone, prior to experience No data collection or computation is necessary . Probability . a posteriori

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Bayesian Networks for Data Mining

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Probability and Simulation

Probability and Simulation. The Study of Randomness. List methods that you can use to predict an event from happening. Dontre getting an A on a Precalc quiz Richard coming to Precalc class for 5 consecutive days. Meha coming to class with starbucks coffee

150 views • 8 slides