Specifying, assessing, and selecting among candidate statistical models is fundamental to
ecological research. Commonly used approaches to model selection are based on …

In linear regression problems with many predictors, penalized regression techniques are
often used to guard against overfitting and to select variables relevant for predicting an …

The process of develo** predictive models includes many stages. Most resources focus
on the modeling algorithms but neglect other critical aspects of the modeling process. This …

Item response theory (IRT) is widely applied in the human sciences to model persons'
responses on a set of items measuring one or more latent constructs. While several R …

Bayesian model averaging is flawed in the M-open setting in which the true data-generating
process is not one of the candidate models being fit. We take the idea of stacking from the …

The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all
observations, model parameters, and model structure using probability theory. Probabilistic …

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 ∗ …

We propose a probabilistic model discovery method for identifying ordinary differential
equations governing the dynamics of observed multivariate data. Our method is based on …

We provide a review of prior distributions for objective Bayesian analysis. We start by
examining some foundational issues and then organize our exposition into priors for: i) …

While it's always possible to compute a variational approximation to a posterior distribution,
it can be difficult to discover problems with this approximation. We propose two diagnostic …