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