Weighted statistics and w parameter
Normally, each statistical function gives an equal weight to each sample value in its parameters. You can use the optional parameter w for any statistical function to specify unequal weights for its samples. This lets you estimate conditional statistics. For example:
Mean(X, w: X > 0)
This computes the mean of X for those samples of X that are positive. In this case, the weight vector contains only zeros and ones. The expression X > 0 gives a weight of 1 (True) for each sample that satisfies the relationship and 0 (False) to those that do not.
By default, this method works over uncertain samples, indexed by Run. You can also use it to compute weighted statistics over other indexes. For example, if Y is an array indexed by J, you could compute:
Mean(Y, I, W: Y > 0)
If you set the system variable SampleWeighting to something other than 1 (see “Importance weights”, all statistical functions use SampleWeighting as the default weights, unless you specify parameter w with some other weighting array. So, when using importance weighting, all statistics (and uncertainty views) automatically use the correct weighting.
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