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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.

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]