23 conditional probabilities

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Unformatted text preview: . . . . . 2.3 Conditional probabilities . . . . . . . . . . . . . . . . . . . . . . 2.4 Independence and the binomial distribution . . . . . . . . . . . 2.4.1 Mutually independent events . . . . . . . . . . . . . . . 2.4.2 Independent random variables (of discrete-type) . . . . 2.4.3 Bernoulli distribution . . . . . . . . . . . . . . . . . . . 2.4.4 Binomial distribution . . . . . . . . . . . . . . . . . . . 2.5 Geometric distribution . . . . . . . . . . . . . . . . . . . . . . . 2.6 Bernoulli process and the negative binomial distribution . . . . 2.7 The Poisson distribution–a limit of Bernoulli distributions . . . 2.8 Maximum likelihood parameter estimation . . . . . . . . . . . . 2.9 Markov and Chebychev inequalities and confidence intervals . . 2.10 The law of total probability, and Bayes formula . . . . . . . . . 2.11 Binary hypothesis testing with discrete-type observations . . . 2.11.1 Maximum likelihood (ML) decision rule . . . . . . . . . 2.11.2 Maximum a posteriori probability (MAP) decision rule . 2...
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This note was uploaded on 02/09/2014 for the course ISYE 2027 taught by Professor Zahrn during the Spring '08 term at Georgia Tech.

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