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5.6 The Central Limit Theorem
There are many cases for which we know the cdf and density of the sum (i.i.d.) For example the Bernoulli, binomial, Poisson, gamma, and Gaussian.
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Preliminary Observations
If m ≠ 0, nm → + or – ∞.
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So, we might consider instead
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Xi ∼ exp(1) RVs implies Yn ∼ Erlang(n,1)
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Derivation of the CLT
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Chapter 7 Bivariate RVs
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Marginal Probabilities
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Marginal Probabilities
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Marginal Probabilities
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Marginal Probabilities
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Marginal Probabilities
Similarly,
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7.2 Jointly Continuous RVs
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Joint and Marginal Densities
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Conversely, if
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