Learning in the Absence of Training Data—A Galactic Application

DOI10.1007/978-3-030-30611-3_5zbMath1435.62442arXiv1811.09204OpenAlexW2990916282MaRDI QIDQ3297244

Dalia Chakrabarty, Cedric Spire

Publication date: 3 July 2020

Published in: Springer Proceedings in Mathematics & Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1811.09204

zbMATH Keywords

Bayesian learningMetropolis-within-Gibbsabsence of training datadark matter in galaxiesstate space density

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Learning and adaptive systems in artificial intelligence (68T05) Applications of statistics to physics (62P35) Statistical astronomy (85A35) Dark matter and dark energy (83C56)

Cites Work

• Bayesian nonparametric estimation of Milky Way parameters using matrix-variate data, in a new Gaussian process based method
• Global classical solutions of the Boltzmann equation with long-range interactions
• Stochastic Processes for Physicists
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