independent random variable वाक्य
उदाहरण वाक्य
मोबाइल
- Under this model the references to stored objects are independent random variables.
- Convolution is used to add two independent random variables defined by distribution functions.
- Let X _ 1, \ ldots, X _ n be independent random variables.
- For such a model, the likelihood function depends on at least one independent random variables.
- Suppose are independent random variables such that
- Notice that this implies that two independent random variables with binomial distributions have to be regarded.
- Here is the covariance, which is zero for independent random variables ( if it exists ).
- The Zeta distribution can be constructed with a sequence of independent random variables with a Geometric distribution.
- X _ 1, \ ldots, X _ n be possibly non-independent random variables.
- Suppose is a sequence of independent random variables, each with finite expected value and variance } }.
- In this aspect, discrete-time martingales generalize the idea of partial sums of independent random variables.
- Similarly to the exponential distribution, the class of PH distributions is closed under minima of independent random variables.
- It follows then that the \ mathcal { F }-correlation between two independent random variables is zero.
- A naive Bayes classifier will assume the pixels are statistically independent random variables and therefore fail to produce good results.
- The probability distribution of the sum of two or more independent random variables is the convolution of their individual distributions.
- A log-normal process is the statistical realization of the multiplicative independent random variables, each of which is positive.
- So that each cumulant of a sum of independent random variables is the sum of the corresponding cumulants of the addends.
- Suppose the number of a man's sons to be a random variable independent random variables, all having the same distribution.
- For any set of independent random variables the probability density function of their joint distribution is the product of their individual density functions.
- Conversely, H ( Y | X ) = H ( Y ) if and only if Y and X are independent random variables.
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