continuous random variableの例文

• The maximal information coefficient uses mutual information on continuous random variables.
• Continuous random variables are defined in terms of intersections of such intervals.
• For justifications of the result for discrete and continuous random variables see.
• If the image is uncountably infinite then X is called a continuous random variable.
• Not all continuous random variables are absolutely continuous, for example a mixture distribution.
• Intuitively, a continuous random variable is the one which can take a statistically is equivalent to zero.
• In this sense, the concept of population can be extended to continuous random variables with infinite populations.
• An example of a continuous random variable would be one based on a spinner that can choose a horizontal direction.
• Thus, this naive definition is inadequate and needs to be changed so as to accommodate the continuous random variables.
• A non-negative continuous random variable " T " represents the time until an event will take place.
• Note that this procedure suggests that the entropy in the discrete sense of a continuous random variable should be  ".
• Or it can be used in probability theory to determine the probability of a continuous random variable from an assumed density function.
• X and Y are independent continuous random variables both uniformly distributed between 0 and some upper limit " a ".
• In general, the probability of a set for a given continuous random variable can be calculated by integrating the density over the given set.
• There exists an upper bound on the entropy of continuous random variables on \ mathbb R with a specified mean, variance, and skew.
• Analogously, for a continuous random variable indicating a continuum of possible states, the value is found by integrating over the state price density.
• However, if X is a continuous random variable and an instance x is observed, \ Pr ( X = x | H ) = 0.
• For distributions " P " and " Q " of a continuous random variable, the Kullback Leibler divergence is defined to be the integral:
• That is, the receiver measures a signal that is equal to the sum of the signal encoding the desired information and a continuous random variable that represents the noise.
• Continuous random variables " X " 1, &, " X n " admitting a joint density are all independent from each other if and only if