The probability density function of a continuous random variable can be regarded as a counterpart of the probability mass function of a discrete random variable.

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The probability density function (PDF) of a continuous random variable assigns probabilities to intervals rather than individual values, and it represents the relative likelihood of the random variable taking a particular value or falling within a range of values. This is analogous to the probability mass function (PMF) of a discrete random variable, which assigns probabilities to individual values. Therefore, the PDF can be regarded as a counterpart of the PMF in the context of continuous random variables.

The Probability Density Function of a Continuous Random Variable Can