Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.
Biometrika , volume=
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New MCMC methods employ data-driven similarity-driven proposals to improve sampling from posteriors on discrete state spaces, extending to hierarchical models without marginalizing latent variables.
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Concentration and Calibration in Predictive Bayesian Inference
Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.
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Similarity-Driven Proposals for MCMC Algorithms on Discrete Spaces
New MCMC methods employ data-driven similarity-driven proposals to improve sampling from posteriors on discrete state spaces, extending to hierarchical models without marginalizing latent variables.