Probability Density#
The relative likelihood of a continuous variable at a value, given by its PDF.
Important
✨ AI-generated content. This page was written with the assistance of an AI language model and is provided as a learning aid. Despite careful review, it may still contain mistakes, omissions, or out-of-date information. Whether you are new to the topic, a team lead, or a senior practitioner, treat it as a starting point rather than an authoritative reference: read it critically and independently verify anything you act on (code, commands, figures, and factual claims) against official documentation and primary sources before relying on it.
What it is#
The probability density function (PDF) \(f(x)\) describes a continuous random variable. It is not a probability itself — its area is: the probability of landing in an interval is the integral
Properties#
The density satisfies \(f(x) \ge 0\), may exceed 1 (it is a density, not a probability), integrates to one over the whole line, and assigns probability zero to any exact value. The bell curve of the normal distribution is the classic PDF.
Link to the CDF#
The density is the derivative of the cumulative distribution function,
so equivalently \(F\) is the running integral of \(f\).
Theme: Probability & Statistics Foundations · All terminology
Hint
Mind map — connected ideas
Probability Mass · Cumulative Distribution Function (CDF) · Probability Distribution · Normal Distribution · Probabilistic Forecasts · Quantile Regression
Hint
More in Probability & Statistics Foundations
Beta Distribution · Confidence Level · Correlation · Critical Value · Cumulative Distribution Function (CDF) · Frequentist · IID (Independent and Identically Distributed) · Likelihood · Margin of Error (MoE) · Mean · Median · Normal Distribution · Outlier · Population Proportion
See also
Source article Adapted (context, re-expressed) in our own words from: Probability Density (insightful-data-lab.com).