| 1. | The maximal information coefficient uses mutual information on continuous random variables.
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| 2. | Continuous random variables are defined in terms of intersections of such intervals.
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| 3. | For justifications of the result for discrete and continuous random variables see.
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| 4. | If the image is uncountably infinite then X is called a continuous random variable.
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| 5. | Not all continuous random variables are absolutely continuous, for example a mixture distribution.
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| 6. | Intuitively, a continuous random variable is the one which can take a statistically is equivalent to zero.
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| 7. | In this sense, the concept of population can be extended to continuous random variables with infinite populations.
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| 8. | An example of a continuous random variable would be one based on a spinner that can choose a horizontal direction.
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| 9. | Thus, this naive definition is inadequate and needs to be changed so as to accommodate the continuous random variables.
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| 10. | A non-negative continuous random variable " T " represents the time until an event will take place.
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