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arXiv Statistics ML By Duy-Minh Dang, Volter Entoma

A data-driven Fourier-mixture neural-network method for density estimation

Read the original on arXiv Statistics ML →

The paper introduces a data‑driven Fourier‑trained neural‑network estimator for fixed‑horizon probability densities, using a positive Gaussian–Laplace mixture with a closed‑form characteristic function. Training occurs directly in Fourier space, enabling non‑negativity and unit‑mass preservation while handling both i.i.d. and resampling‑based pseudo‑sampling data. The authors provide theoretical error bounds, a multidimensional extension, and demonstrate competitive empirical performance against Expectation–Maximization, especially on heavy‑tailed targets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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