Description: Probability density estimation is a statistical technique used to infer the probability distribution of a random variable from a dataset. Unlike supervised learning methods, which require predefined labels or categories, probability density estimation falls under unsupervised learning, where the goal is to discover underlying patterns and structures in the data without prior information. This technique allows for modeling how data is distributed in space, providing a continuous representation of probability. There are several methods to carry out this estimation, including histograms, kernel density estimation, and parametric models such as the normal distribution. Probability density estimation is fundamental in various fields, as it allows for making inferences about the population from samples, identifying anomalies, and facilitating decision-making based on the likelihood of future events. Its ability to provide a more comprehensive view of data distribution makes it a valuable tool in exploratory analysis and statistical modeling.
