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</center><p>How do we jointly specify multiple r.v.s, i.e., be able to determine the probability of any event involving multiple r.v.s? In this section, we'll see how to generalize the Binomial, and in the next, the Normal This will be called the joint distribution of two or more random variables. Usually, we take the min, max or median of a set of random variables and do computations with themso, it would be useful if we had a general formula for the PDF  Define multiple random variables in terms of their PDF and CDF and calculate joint moments such as the correlation and covariance. We start with an example to nd the distribution of Y(n) = Ymax, the largest order statistic As you've seen, the Binomial distribution is extremely commonly used, and probably the most important discrete distribution. (From \Probability & Statistics with Applications to Computing" by Alex Tsun) This is de nitely one of the most important sections in the entire text! With more than one random variable, the set of outcomes is an N-dimensional space, Sx = {−∞Two Discrete Random Variables – Joint PMFs As we have seen, one can deﬁne several r.v.s on the sample space of a random experiment. Objectives. In this section, we’ll focus on joint discrete distributions, and in the next, joint continuous distributions The Central Limit Theorem is used everywhere in statistics (hypothesis testing), and it also has its applications in computing probabilities Usually, we take the min, max or median of a set of random variables and do computations with themso, it would be useful if we had a general formula for the PDF and CDF of the min or max. We ﬁrst consider two discrete r.v.s Let X and Y be two discrete random Multiple Random VariablesLimit Theorems. The Normal distribution is certainly the most important continuous distribution. Introduce Joint  Multiple Random Variables Covariance and Correlation (From \Probability & Statistics with Applications to Computing" by Alex Tsun) In this section, we’ll learn  Multiple random variablesIn many problems, we are interested in more than one random variables representing di®erent quantities of interest from the same  II.D Many Random Variables.</p></translate>
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