In this aspect, discrete-time martingales generalize the idea of partial sums of independent random variables.
12.
Similarly to the exponential distribution, the class of PH distributions is closed under minima of independent random variables.
13.
It follows then that the \ mathcal { F }-correlation between two independent random variables is zero.
14.
A naive Bayes classifier will assume the pixels are statistically independent random variables and therefore fail to produce good results.
15.
The probability distribution of the sum of two or more independent random variables is the convolution of their individual distributions.
16.
A log-normal process is the statistical realization of the multiplicative independent random variables, each of which is positive.
17.
So that each cumulant of a sum of independent random variables is the sum of the corresponding cumulants of the addends.
18.
Suppose the number of a man's sons to be a random variable independent random variables, all having the same distribution.
19.
For any set of independent random variables the probability density function of their joint distribution is the product of their individual density functions.
20.
Conversely, H ( Y | X ) = H ( Y ) if and only if Y and X are independent random variables.
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