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Aki Vehtari develops computational Bayesian statistics, including probabilistic programming, model diagnostics, model assessment and Gaussian processes. His author research page describes these as connected parts of a Bayesian workflow: specify a model, compute its implications, check the computation and examine predictive adequacy.^[ref]

He coauthored Bayesian Data Analysis by Andrew Gelman and others. A posterior distribution summarizes uncertainty conditional on the model and data; checking whether that model reproduces important observations is a further task. This distinction connects Bayesian inference with posterior predictive checks, rather than treating successful computation as sufficient evidence that the model is suitable.^[ref]

David Dunson develops Bayesian inference and statistical machine learning for complex data. His research seeks lower-dimensional structure in observations such as networks, functions and biological measurements, with applications in ecology, neuroscience and environmental health.^[ref]

A model may represent many dependent measurements through a smaller set of latent quantities, then carry uncertainty about those quantities into predictions. Dunson's work couples flexible modelling with algorithms for Bayesian inference and uncertainty quantification. He is also a coauthor of Bayesian Data Analysis by Andrew Gelman and others.^[ref]

Hal S. Stern is a statistician whose research includes Bayesian statistics and the analysis of forensic evidence. He is a coauthor of Bayesian Data Analysis by Andrew Gelman and others and of the article with Andrew Gelman explaining why a difference between statistical significance and nonsignificance need not itself be statistically significant. This distinction separates the comparison of two estimated effects from two separate hypothesis tests.^[ref]

John Carlin develops biostatistics for health and medical research, including methods for missing data and causal inference. His institutional profiles describe work on multiple imputation and the need to specify the scientific purpose of an analysis before choosing a regression model.^[ref][ref]

With multiple imputation, several plausible completed datasets represent uncertainty about unobserved values. Their analyses must account both for uncertainty within a completed dataset and for variation between completed datasets. This makes the assumptions about the missing values visible in the analysis. Carlin is a coauthor of Bayesian Data Analysis by Andrew Gelman and others.^[ref]