1 citations · 1 across the 3 of their papers we have counts for
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Robust model selection using likelihood as data
Jongwoo Choi, Neil A. Spencer, Jeffrey W. Miller
Model selection is a central task in statistics, but standard methods are not robust in misspecified settings where the true data-generating process (DGP) is not in the set of cand…
Bayesian model criticism using uniform parametrization checks
Christian T. Covington, Jeffrey W. Miller
Models are often misspecified in practice, making model criticism a key part of Bayesian analysis. It is important to detect not only when a model is wrong, but which aspects are w…
Reproducible Parameter Inference Using Bagged Posteriors
Jonathan H. Huggins, Jeffrey W. Miller
Under model misspecification, it is known that Bayesian posteriors often do not properly quantify uncertainty about true or pseudo-true parameters. Even more fundamentally, misspec…