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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]

By Andrew Gelman, John Carlin, Hal S. Stern, David Dunson, Aki Vehtari and Donald Rubin, as credited on the author-hosted book homepage. Third edition.

Official homepage: sites.stat.columbia.edu/gelman/book/

The homepage supplies the author list and an authorized PDF for noncommercial use. Cited here for the coauthorship of Hal S. Stern and the textbook’s Bayesian subject.