| 1. | Under this model the references to stored objects are independent random variables.
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| 2. | Convolution is used to add two independent random variables defined by distribution functions.
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| 3. | Let X _ 1, \ ldots, X _ n be independent random variables.
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| 4. | For such a model, the likelihood function depends on at least one independent random variables.
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| 5. | Suppose are independent random variables such that
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| 6. | Notice that this implies that two independent random variables with binomial distributions have to be regarded.
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| 7. | Here is the covariance, which is zero for independent random variables ( if it exists ).
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| 8. | The Zeta distribution can be constructed with a sequence of independent random variables with a Geometric distribution.
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| 9. | X _ 1, \ ldots, X _ n be possibly non-independent random variables.
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| 10. | Suppose is a sequence of independent random variables, each with finite expected value and variance } }.
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