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probability density function
This term is a technical cornerstone of statistics and probability theory, specifically used for continuous random variables. It differs from a probability mass function, which is used for discrete variables. A critical distinction is that the value of the function at a specific point does not represent the probability of that exact value occurring—which is always zero for continuous variables—but rather the relative likelihood or density.
In practical application, the probability is found by calculating the integral of the function over a specific range. This means the total area under the entire curve must always equal one. It is most commonly encountered in data science, physics, and engineering when modeling phenomena like height, weight, or measurement errors.
Meanings
A function that describes the relative likelihood for a continuous random variable to take on a given value, where the area under the curve over an interval represents the probability of the variable falling within that interval.
The probability density function of a normal distribution is characterized by a bell-shaped curve.