Probability Density Functions

A Probability Density Function (PDF) is a statistical function that describes the likelihood of a continuous random variable taking on a particular value. The PDF is characterized by its integral over a range of values, which equals one, ensuring that the total probability is conserved. Commonly used in statistics and data analysis, PDFs help in understanding the distribution of data points, making them essential for various applications such as hypothesis testing, Bayesian statistics, and machine learning. In practice, PDFs are often visualized as curves on a graph, where the area under the curve represents probabilities.