Importance weighting is a general way to adjust Monte Carlo integration to account for
draws from the wrong distribution, but the resulting estimate can be highly variable when the …

Numerous studies have demonstrated the strong out-of-sample predictive ability of machine
learning models, particularly in variable selection and dimension reduction, on crude oil …

The increasing availability of massive data sets poses various challenges for machine
learning. Prominent among these is learning models under hardware or human resource …

Here are some answers by Aki Vehtari to frequently asked questions about cross-validation
and loo package. If you have further questions, please ask them in Stan discourse thread …

The rising integration of AI-driven assessment in education holds promise, yet it is crucial to
evaluate the correlation between trust in general AI tools, AI-based scoring systems, and …

Grappling With Uncertainty in Forecasting the 2024 US Presidential Election · Issue 6.4, Fall
2024 Skip to main content Search Dashboard caret-down LoginLogin or Signup Home Issues …

Importance sampling (IS) is widely used for approximate Bayesian cross validation (CV) due
to its efficiency, requiring only the re-weighting of a single set of posterior draws. With …

The use of non-differentiable priors is standard in modern parsimonious Bayesian models.
Lack of differentiability, however, precludes gradient-based Markov chain Monte Carlo …

Generalization, the transfer of knowledge to novel situations, has been studied in distinct
disciplines that focus on different aspects. Here we propose a Bayesian model that assumes …

We propose a general framework for obtaining probabilistic solutions to PDE-based inverse
problems. Bayesian methods are attractive for uncertainty quantification but assume …