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Robust Multi-Modal Density Estimation
May 7, 2024, 4:45 a.m. | Anna M\'esz\'aros, Julian F. Schumann, Javier Alonso-Mora, Arkady Zgonnikov, Jens Kober
cs.LG updates on arXiv.org arxiv.org
Abstract: The estimation of probability density functions is a fundamental problem in science and engineering. However, common methods such as kernel density estimation (KDE) have been demonstrated to lack robustness, while more complex methods have not been evaluated in multi-modal estimation problems. In this paper, we present ROME (RObust Multi-modal Estimator), a non-parametric approach for density estimation which addresses the challenge of estimating multi-modal, non-normal, and highly correlated distributions. ROME utilizes clustering to segment a multi-modal …
abstract arxiv cs.lg engineering estimator functions fundamental however kernel modal multi-modal paper probability robust robustness science stat.ml type while
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