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Finite mixture models are typically inconsistent for the number of components

, , and . (2020)cite arxiv:2007.04470Comment: 16 pages, 1 figure.

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Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees., , , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 796-805. PMLR, (2019)Parallel Tempering With a Variational Reference., , , and . NeurIPS, (2022)Coreset Markov chain Monte Carlo., and . AISTATS, volume 238 of Proceedings of Machine Learning Research, page 4438-4446. PMLR, (2024)Multiagent allocation of Markov decision process tasks., , and . ACC, page 2356-2361. IEEE, (2013)Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture., , , , and . CoRR, (2020)MixFlows: principled variational inference via mixed flows., , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 38342-38376. PMLR, (2023)Finite mixture models do not reliably learn the number of components., , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 1158-1169. PMLR, (2021)Sparse Variational Inference: Bayesian Coresets from Scratch., and . NeurIPS, page 11457-11468. (2019)Slice Sampling for General Completely Random Measures., , and . UAI, volume 124 of Proceedings of Machine Learning Research, page 699-708. AUAI Press, (2020)General bounds on the quality of Bayesian coresets.. CoRR, (2024)