Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
GetDist implements boundary-corrected KDE with automatic smoothing for analyzing weighted and correlated Monte Carlo samples, plus plotting and diagnostic tools.
citing papers explorer
-
GetDist: a Python package for analysing Monte Carlo samples
GetDist implements boundary-corrected KDE with automatic smoothing for analyzing weighted and correlated Monte Carlo samples, plus plotting and diagnostic tools.