Maximum Number of Modes of Gaussian Mixtures
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Abstract (arXiv)
Gaussian mixture models are widely used in Statistics. A fundamental aspect of these distributions is the study of the local maxima of the density, or modes. In particular, it is not known how many modes a mixture of k Gaussians in d dimensions can have. We give a brief account of this problem's history. Then, we give improved lower bounds and the first upper bound on the maximum number of modes, provided it is finite.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Feb 2017.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 0; 2025: 2.
7 papers.
- 2025The Information Dynamics of Generative Diffusioncited by 12
- 2023Self-Asymmetric Invertible Network for Compression-Aware Image Rescalingcited by 9
- 2023Convergence Analysis of Mean Shiftcited by 6
- 2021Fast approximations of the Jeffreys divergence between univariate Gaussian mixture models via exponential polynomial densitiescited by 3
- 2025CoCoAFusE: Beyond Mixtures of Experts via Model Fusioncited by 1
- 2022GMM-IL: Image Classification using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizescited by 0
- 2023Dimension-Independent Kernel ε-Coverscited by 0
- 2025The Information Dynamics of Generative Diffusion27 Aug 2025
- 2025CoCoAFusE: Beyond Mixtures of Experts via Model Fusion2 May 2025
- 2023Dimension-Independent Kernel ε-Covers28 Jun 2023
- 2023Convergence Analysis of Mean Shift15 May 2023
- 2023Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling4 Mar 2023
- 2022GMM-IL: Image Classification using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes1 Dec 2022
- 2021Fast approximations of the Jeffreys divergence between univariate Gaussian mixture models via exponential polynomial densities13 Jul 2021