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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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Finite mixture models are typically inconsistent for the number of components, , and . (2020)cite arxiv:2007.04470Comment: 16 pages, 1 figure.Approximate Decentralized Bayesian Inference., and . UAI, page 102-111. AUAI Press, (2014)Pigeons.jl: Distributed Sampling From Intractable Distributions., , , , , and . CoRR, (2023)Bayesian inference via sparse Hamiltonian flows., , and . NeurIPS, (2022)The CPD Data Set: Personnel, Use of Force, and Complaints in the Chicago Police Department., , , , and . NeurIPS Datasets and Benchmarks, (2021)Bayesian Pseudocoresets., , , and . NeurIPS, (2020)Universal Boosting Variational Inference., and . NeurIPS, page 3479-3490. (2019)Validated Variational Inference via Practical Posterior Error Bounds, , , and . (2019)cite arxiv:1910.04102Comment: A python package for carrying out our validated variational inference workflow -- including doing black-box variational inference and computing the bounds we develop in this paper -- is available at https://github.com/jhuggins/viabel. The same repository also contains code for reproducing all of our experiments.Validated Variational Inference via Practical Posterior Error Bounds., , , and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 1792-1802. PMLR, (2020)Decentralized Variational Bayesian Inference., and . CoRR, (2014)