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Bayesian Approaches to Finding the Needles in the Microscopy Haystack
Published online by Cambridge University Press: 30 July 2021
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- Full System and Workflow Automation for Enabling Big Data and Machine Learning in Electron Microscopy
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- Copyright
- Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America
References
[2] Vlachos, A., Ghahramani, Z., and Korhonen, A., “Dirichlet Process Mixture Models for Verb Clustering,” Proc. ICML Work. Prior Knowl. Text Lang. Process. Helsinki, Finl., pp. 1–6, 2008, [Online]. Available: papers3://publication/uuid/612EDB87-A804-46C9-8254-BF5351273773.Google Scholar
Sethuraman, J., “A CONSTRUCTIVE DEFINITION OF DIRICHLET PRIORS,” Inst. Stat. Sci., vol. 4, no. 2, pp. 639–650, 1994.Google Scholar
Kurihara, K., Welling, M., and Teh, Y. W., “Collapsed variational dirichlet process mixture models,” IJCAI Int. Jt. Conf. Artif. Intell., pp. 2796–2801, 2007.Google Scholar
Bouman, C. A., “Cluster: An unsupervised algorithm for modeling Gaussian mixtures.” 1997.Google Scholar
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