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The MLFD is seen as a weighted Poisson distribution as follows: If X ~ Poisson having pmf Pleft( X= kright)={e}^{-lambda}{lambda}^k/ k!, k=0,1,2,cdots; lambda >0, then for integer α and β it can be shown that weighted distribution with weight function 1/ k + 1)(α − 1 k + β − 1 gives the pmf of MLFD.
To distribute the generated objects in naturally looking clusters, we use the weighted distribution method described in the Population Distribution Generation section with randomly generated weight-maps.
Sample weights also include a poststratification adjustment so that the weighted distribution of controls across the matching variables matched exactly the weighted distribution of cases.
We call W a weight-map and a distribution created using W a weighted distribution.
For controls, their sample weights also include a poststratification adjustment so that the weighted distribution of controls across the matching variables matches the weighted distribution of cases in an exact manner.
Weighted distribution of discrete normal with parameter (λ, q) with weight function of the form π x is again discrete normal (πλ, q).
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The weighted distribution-free optimization scheme was repeated to compound information from a key lean process indicator, the drilling processing time.
Distributions of this type are known as weighted distributions ( see, e.g. Cox [1962]; Fisher [1934]; Patil and Ord [1976]; Rao [1985]).
We used these particles to generate the weighted distributions of azimuth and speed from which random steps were selected (Fig 1d).
During the Scale paradigm, there was no difference between subjects in absolute range of notes played (highest or lowest note) for ScaleCtrl or ScaleImprov and no statistical difference between weighted distributions of notes.
Examples of weighted distributions can be seen in Figure 8.
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