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I am trying to find an example that clearly shows that the kernel density estimator does worse than the Dirichlet process in terms of estimating the distribution of a sample. But eventually, I always find out that both behave similarly, especially and I tune the bandwidth of the kernel density estimator.

Does anyone have any idea about a sample that the Dirichlet process is clearly more efficient that kernel density estimator ??

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