# 363 the central limit theorem and

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Unformatted text preview: 3.6.3 The central limit theorem and the Gaussian approximation 3.7 ML parameter estimation for continuous-type variables . . . . . . . 3.8 Functions of a random variable . . . . . . . . . . . . . . . . . . . . 3.8.1 The distribution of a function of a random variable . . . . . 3.8.2 Generating a random variable with a speciﬁed distribution 3.8.3 The area rule for expectation based on the CDF . . . . . . 3.9 Failure rate functions . . . . . . . . . . . . . . . . . . . . . . . . . . 3.10 Binary hypothesis testing with continuous-type observations . . . . 4 Jointly Distributed Random Variables 4.1 Joint cumulative distribution functions . . . . . . . . . . . . . 4.2 Joint probability mass functions . . . . . . . . . . . . . . . . 4.3 Joint probability density functions . . . . . . . . . . . . . . . 4.4 Independence of random variables . . . . . . . . . . . . . . . 4.4.1 Deﬁnition of independence for two random variables . 4.4.2 Determining from a pdf whether independence holds . 4.5 Distribution o...
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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 Institute of Technology.

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