Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models

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Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors (2018, background): “Specifying priors is intuitive for small probabilistic models, since each variable typically has a clear interpretation (Blei, 2014).”
Edward: A library for probabilistic modeling, inference, and criticism (2016, methodology): “As we criticize our model’s fit to the data, we revise components of the model and repeat to form an iterative loop (Box, 1976; Blei, 2014; Gelman et al., 2013).”
Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation (2020, methodology): “(2013) and Blei (2014). In particular, we formulate separate hierarchical models to inspect length, lexical, and word order statistics: sequence length, unigram and bigram counts, and skip-bigram counts, respectively.”
The Blessings of Multiple Causes (2018, methodology): “More broadly, the process of iterative model building—cycling between finding, fitting, and checking a model of the assignments—relates to the applied practice of Bayesian data analysis (Gelman et al. 2013; Blei 2014).”
Statistical Models (2014, background): “Gelman et al. (1995) give a good summary of the process, another perspective on " Box's loop " (Blei, 2014).”
Noise Contrastive Priors for Functional Uncertainty (2018, background): “Specifying priors is intuitive for small probabilistic models, where each variable often has a clear interpretation (Blei, 2014).”