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The distribution is called folded because the probability mass (that is, area) to the left of the point x = 0 is folded over by taking the absolute value.
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In the discrete case the weights are given by the probability mass function, and in the continuous case the weights are given by the probability density function.
In the continuous case, the counterpart of the probability mass function is the probability density function, also denoted by f(x).
The probability mass function can be computed as (66).
The number of intervals among which the probability mass is distributed is five and it was preferred to offer the respondent ten points to distribute as opposed to 100 because it is cognitively less demanding.
Finally, the probability mass in the tails of the background distribution is added up as a wrap-around interval, which also holds a probability mass of 1/N.
With learning, the probability mass shifts toward higher probabilities.
The probability mass is initially collected near zero.
Note that this estimator did spend 100% of the probability mass.
The probability mass function is: (6) A sample from this distribution defines n ijkl (t + 1).
The binomial probability mass function (equation 6) provides the probability that x successes will occur in n trials of a binomial experiment.
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