Kernel embedding of distributions

Sat, 18 Jul 2026 10:03:27 +0000

Definition

Kernel Embedding of Distributions allows probabilistic objects to be treated as points in a high-dimensional feature space called a Reproducing Kernel Hilbert Space (RKHS). By mapping distributions to mean embeddings, complex statistical operations like computing distances between distributions or conditional expectations become linear algebra problems. This approach facilitates non-parametric statistical inference and is crucial in advanced machine learning tasks involving distributional data, such as two-sample testing and causal inference.

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Kernel embedding of distributions

Definition