TAILIEUCHUNG - Lecture Discrete mathematics and its applications (7/e) – Chapter 7: Discrete probability

Lecture Discrete mathematics and its applications (7/e) – Chapter 7: Discrete probability. This chapter presents the following content: Introduction to discrete probability, probability theory, Bayes’ theorem, expected value and variance. | Discrete Probability Chapter 7 With Question/Answer Animations Copyright © McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education. Chapter Summary Introduction to Discrete Probability Probability Theory Bayes’ Theorem Expected Value and Variance An Introduction to Discrete Probability Section Section Summary Finite Probability Probabilities of Complements and Unions of Events Probabilistic Reasoning Probability of an Event We first study Pierre-Simon Laplace’s classical theory of probability, which he introduced in the 18th century, when he analyzed games of chance. We first define these key terms: An experiment is a procedure that yields one of a given set of possible outcomes. The sample space of the experiment is the set of possible outcomes. An event is a subset of the sample space. Here is how Laplace defined the probability of an event: Definition: If S is a finite sample space of equally likely outcomes, and E is an event, that is, a subset of S, then the probability of E is p(E) =

• Toán học
• Discrete mathematics
• Modeling computation
• Number theory
• Advanced counting techniques
• Discrete probability
• Probability theory
• Bayes’ theorem
• Discrete mathematics applications
• Discrete Structures
• Mathematical Reasoning
• Lecture Discrete Mathematics I
• Discrete Mathematics I
• Bài giảng Toán rời rạc
• Probability rules
• andom variables
• Probability models
• Combinatorial Analysis
• Computer Science
• Applications discrete mathematics
• The foundations
• Basic structures
• The integers