Prior Knowledge Elicitation: The Past, Present, and Future Page 1 Bayesian Analysis (2024)
19, Number 4, pp. 1129–1161 Prior Knowledge Elicitation: The Past, Present, and Future ∗ …

Determining the sensitivity of the posterior to perturbations of the prior and likelihood is an
important part of the Bayesian workflow. We introduce a practical and computationally …

Design conditions for marine structures are typically informed by threshold-based extreme
value analyses of oceanographic variables, in which excesses of a high threshold are …

Mixture priors provide an intuitive way to incorporate historical data while accounting for
potential prior-data conflict by combining an informative prior with a noninformative prior …

Uncertainty quantification and robustness to distribution shifts are important goals in
machine learning and artificial intelligence. Although Bayesian Neural Networks (BNNs) …

Drug developers are required to demonstrate substantial evidence of effectiveness through
the conduct of adequate and well‐controlled (A&WC) studies to obtain marketing approval of …

Bayesian methods allow borrowing of historical information through prior distributions. The
concept of prior effective sample size (prior ESS) facilitates quantification and …

When using complex Bayesian models to combine information, checking consistency of the
information contributed by different components of the model for inference is good statistical …

In many orphan diseases and pediatric indications, the randomized controlled trials may be
infeasible because of their size, duration, and cost. Leveraging information on the control …

Scientific inquiries seek evidence to establish, support, or challenge theories and
arguments. Therefore, hypothesis testing and statistical estimation, including prediction and …