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Found 6 papers, 1 papers with code
Gibbs posterior concentration rates under sub-exponential type losses
Gibbs posterior distributions, on the other hand, offer direct, principled, probabilistic inference on quantities of interest through a loss function, not a model-based likelihood.
Statistics Theory Methodology Statistics Theory 62F15 (Primary) 62G08, 62G20 (Secondary)
Variational approximations of empirical Bayes posteriors in high-dimensional linear models
In high-dimensions, the prior tails can have a significant effect on both posterior computation and asymptotic concentration rates.
Validity, consonant plausibility measures, and conformal prediction
The standard notion of validity, what we refer to here as Type-1 validity, focuses on coverage probability of prediction regions, while a notion of validity relevant to the other prediction-related tasks performed by predictive distributions is lacking.
Variational approximations using Fisher divergence
Modern applications of Bayesian inference involve models that are sufficiently complex that the corresponding posterior distributions are intractable and must be approximated.
Calibrating general posterior credible regions
An advantage of methods that base inference on a posterior distribution is that credible regions are readily obtained.
Methodology
