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When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo (MCMC) algorithms have difficulty moving between modes, and default variational or mode-based approximate inferences will understate posterior uncertainty.
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Hansmann, U. H. (1997) · 1997
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Weak convergence and optimal scaling of random walk Metropolis algorithms
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Vehtari, A., Särkkä, S., and Lampinen, J. (2000) · 2000
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Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003) · 2003
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Clarke, B. (2003) · 2003
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Liu, J. and Hodges, J. S. (2003) · 2003
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Oelrich, O., Ding, S., Magnusson, M., Vehtari, A., and Villani, M. (2020) · 2003
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Torpid mixing of simulated tempering on the Potts model
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Earl, D. J. and Deem, M. W. (2005) · 2005
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Tipping, M. E. and Lawrence, N. D. (2005) · 2005
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Bayesian selection of misspecified models is overconfident and may cause spurious posterior probabilities for phylogenetic trees
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