= Aki Vehtari
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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.https://users.aalto.fi/~ave/[Author research interests]{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.<Bayesian Data Analysis by Andrew Gelman and others>[Third-edition author list and model-checking exposition]{ref}
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