Density Estimation
Density Estimation is a process of estimating the probability density function of a random variable from a set of data. It is a fundamental concept in statistics and machine learning that involves modeling the underlying distribution of a dataset. The goal of density estimation is to estimate the probability density function of a random variable from a set of observations. This can be done using various techniques such as kernel density estimation, histogram-based methods, and parametric methods. Density estimation is used in a wide range of applications such as anomaly detection, clustering, and classification.
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