The method of model averaging has become an important tool to deal with model
uncertainty, for example in situations where a large amount of different theories exist, as are …

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) …

Probability models are used for many statistical tasks, notably parameter estimation, interval
estimation, inference about model parameters, point prediction, and interval prediction …

Bayesian model selection procedures based on nonlocal alternative prior densities are
extended to ultrahigh dimensional settings and compared to other variable selection …

Bayes factors represent a useful alternative to P-values for reporting outcomes of hypothesis
tests by providing direct measures of the relative support that data provide to competing …

Statisticians often face the choice between using probability models or a paradigm defined
by minimising a loss function. Both approaches are useful and, if the loss can be re-cast into …

This article is motivated by the problem of inference on interactions among chemical
exposures impacting human health outcomes. Chemicals often co-occur in the environment …

The Median Probability Model and Correlated Variables Page 1 Bayesian Analysis (2021) 16,
Number 4, pp. 1085–1112 The Median Probability Model and Correlated Variables Maria M …

Summary We propose a Monte Carlo algorithm to sample from high dimensional probability
distributions that combines Markov chain Monte Carlo and importance sampling. We provide …

With high-dimensional data emerging in various domains, sparse logistic regression models
have gained much interest of researchers. Variable selection plays a key role in both …