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Peer-reviewedLicence not statedPhysica D Nonlinear Phenomena

KGMM: A K-means clustering approach to Gaussian mixture modeling for score function estimation

  • L. T. Giorgini1
  • Tobias Bischoff2
  • Andre N. Souza3
  1. 1Department of Mathematics, MIT, Cambridge, MA, USA
  2. 2Aeolus Labs, San Francisco, CA, USA
  3. 3Department of Earth, Atmospheric, and Planetary Sciences, MIT, Cambridge, MA, USA
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Cite

BibTeXRIS
BibTeX
@article{giorgini2026kgmm,
  title = {{KGMM: A K-means clustering approach to Gaussian mixture modeling for score function estimation}},
  author = {L. T. Giorgini and Tobias Bischoff and Andre N. Souza},
  journal = {Physica D Nonlinear Phenomena},
  year = {2026},
  month = may,
  doi = {10.1016/j.physd.2026.135274},
  url = {https://doi.org/10.2139/ssrn.5206637},
}
RIS
TY  - JOUR
TI  - KGMM: A K-means clustering approach to Gaussian mixture modeling for score function estimation
AU  - L. T. Giorgini
AU  - Tobias Bischoff
AU  - Andre N. Souza
PY  - 2026
DA  - 2026/05/16
JO  - Physica D Nonlinear Phenomena
DO  - 10.1016/j.physd.2026.135274
UR  - https://doi.org/10.2139/ssrn.5206637
SN  - 0167-2789
SN  - 1872-8022
ER  -