A Bregman-divergence generalization of ELPD enables robust predictive model selection by tuning sensitivity to tail mismatch via a parameter β.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
New discrete-time approximations to SG(L)D enable accurate non-asymptotic predictions of covariance and integrated autocorrelation time for practical tuning in large-batch or misspecified regimes.
citing papers explorer
-
Robust Bayesian Predictive Model Selection using Bregman Divergence
A Bregman-divergence generalization of ELPD enables robust predictive model selection by tuning sensitivity to tail mismatch via a parameter β.
-
Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo
New discrete-time approximations to SG(L)D enable accurate non-asymptotic predictions of covariance and integrated autocorrelation time for practical tuning in large-batch or misspecified regimes.