Assertion: distribution's density function | /r/HasProperty | pdf
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Source sentences:
text: Since we look at regions in which a given outcome is likely to occur, we define the Probability Density Function (PDF) as the a function that describes the probability that a given outcome will occur at a given point.
text: The corresponding probability density function (PDF) is:
text: From the cumulative distribution function (CDF) one can derive a histogram and the probability density function (PDF).
text: For most values of the shape parameter, λ, the probability density function (PDF) and cumulative distribution function (CDF) must be computed numerically.
text: The result is that X {\displaystyle X} has the following probability density function (pdf) for x > 0 {\displaystyle x>0} :
text: The probability density function (pdf) of the normal distribution , also called Gaussian or "bell curve", the most important continuous random distribution.
text: The probability density function (pdf) of an exponential distribution is
text: The probability density function (pdf) of the chi-square distribution is
text: The probability density function (pdf) of the beta distribution, for 0 ≤ x ≤ 1, and shape parameters α, β > 0, is a power function of the variable x and of its reflection (1 − x) as follows: