Multivariate kernel density estimation (KDE) is a non-parametric way to estimate the probability density function (PDF) of a random vector in multiple dimensions. It generalizes the univariate kernel density estimation, which aims to estimate the density function from a sample of data points in one dimension, to cases where data is in two or more dimensions. ### Key Concepts: 1. **Kernel Function**: - A kernel function is a symmetric, non-negative function that integrates to one.
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