'Conditional probability mass function' diaporamas de présentation

Chapter 3. Discrete Probability Distributions

Chapter 3. Discrete Probability Distributions. 3.1 The Binomial Distribution 3.2 The Geometric and Negative Binomial Distributions 3.3 The Hypergeometric Distribution 3.4 The Poisson Distribution 3.5 The Multinomial Distribution.

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Discrete Random Variable

Discrete Random Variable. Outline. Expected Value (Section 2.5 ) Functions of a Random Variable (Section 2.6) Expected Value of a Derived Random Variable (Section 2.7) Variance and Standard of Deviation (Section 2.8) Conditional Probability Mass Function (Section 2.9). Expected Value.

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Chapter 5 Joint Probability Distribution

Chapter 5 Joint Probability Distribution. Introduction. If X and Y are two random variables, the probability distribution that defines their simultaneous behavior is a Joint Probability Distribution. Examples: Signal transmission: X is high quality signals and Y low quality signals.

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Conditional probability mass function

Conditional probability mass function. Discrete case Continuous case. Conditional probability mass function - examples. Throwing two dice Let Z 1 = the number on the first die Let Z 2 = the number on the second die Set Y = Z 1 and X = Z 1 + Z 2 Radioactive decay

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Continuous random variables

Continuous random variables. Continuous random variable Let X be such a random variable Takes on values in the real space (-infinity; +infinity) (lower bound; upper bound) Instead of using P(X= i ) Use the probability density function f X (t) Or f X (t) dt.

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P robability Models & Applications

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