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Computer Science

108Sample Spaces and Events
109Probability Axioms
110Computing Basic Probabilities
111Union and Intersection of Events
112Complement Rule
113Addition Rule for Probabilities
114Conditional Probability
115Multiplication Rule
116Independent Events
117Law of Total Probability
118Bayes' Theorem
119Probability in Algorithm Analysis
120Random Variables: Definition and Types
121Discrete Random Variables
122Probability Mass Functions (PMF)
123Cumulative Distribution Functions (CDF)
124Bernoulli Distribution
125Binomial Distribution
126Geometric Distribution
127Continuous Random Variables and PDF
128Uniform Distribution
129Exponential Distribution
130Definition of Expected Value
131Expected Value of Functions
132Linearity of Expectation
133Variance: Measuring Spread
134Computing Variance from Moments
135Standard Deviation
136Variance of Linear Transformations
137Expected Value and Variance in Algorithm Analysis

Computer Science

108Sample Spaces and Events
109Probability Axioms
110Computing Basic Probabilities
111Union and Intersection of Events
112Complement Rule
113Addition Rule for Probabilities
114Conditional Probability
115Multiplication Rule
116Independent Events
117Law of Total Probability
118Bayes' Theorem
119Probability in Algorithm Analysis
120Random Variables: Definition and Types
121Discrete Random Variables
122Probability Mass Functions (PMF)
123Cumulative Distribution Functions (CDF)
124Bernoulli Distribution
125Binomial Distribution
126Geometric Distribution
127Continuous Random Variables and PDF
128Uniform Distribution
129Exponential Distribution
130Definition of Expected Value
131Expected Value of Functions
132Linearity of Expectation
133Variance: Measuring Spread
134Computing Variance from Moments
135Standard Deviation
136Variance of Linear Transformations
137Expected Value and Variance in Algorithm Analysis

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Lesson 108 of 2,872·3. Probability for Computer SciencePro lesson

Sample Spaces and Events

Define sample spaces, outcomes, and events as the foundation for probability theory.

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