marginal distribution sentence in Hindi
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- A test that relies on different assumptions is Fisher's exact test; if its assumption of fixed marginal distributions is met it is substantially more accurate in obtaining a significance level, especially with few observations.
- The argument is sometimes expressed in an alternative way by having the posterior marginal distribution of " n " based on " N " without explicitly invoking a non-zero chance of existing.
- The D4? width, unlike the FWHM and 1 / e 2 widths, is meaningful for multimodal marginal distributions that is, beam profiles with multiple peaks but requires careful subtraction of the baseline for accurate results.
- Factor graphs can be combined with message passing algorithms to efficiently compute certain characteristics of the function g ( X _ 1, X _ 2, \ dots, X _ n ), such as the marginal distributions.
- \boldsymbol \ Sigma _ { XX } and \ boldsymbol \ Sigma _ { YY } can be identified as the variance matrices of the marginal distributions for \ boldsymbol { X } and \ boldsymbol { Y } respectively.
- Measurements of the 1 / e 2 width only depend on three points on the marginal distribution, unlike D4? and knife-edge widths that depend on the integral of the marginal distribution . 1 / e 2 width measurements are noisier than D4? width measurements.
- Measurements of the 1 / e 2 width only depend on three points on the marginal distribution, unlike D4? and knife-edge widths that depend on the integral of the marginal distribution . 1 / e 2 width measurements are noisier than D4? width measurements.
- The distribution of the marginal variables ( the marginal distribution ) is obtained by "'marginalizing "'over the distribution of the variables being discarded, and the discarded variables are said to have been "'marginalized out " '.
- Given two random variables " X " and " Y " whose joint distribution is known, the marginal distribution of " X " is simply the probability distribution of " X " averaging over information about " Y ".
- The copula " C " contains all information on the dependence structure between the components of ( X _ 1, X _ 2, \ dots, X _ d ) whereas the marginal cumulative distribution functions F _ i contain all information on the marginal distributions.
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