An Introduction to Bayesian Inference via Variational Approximations

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Variational Inference: A Review for Statisticians (2016, background): “…control and reinforcement learning (Van Den Broek, Wiegerinck, and Kap-pen 2008; Furmston and Barber 2010), statistical network analysis (Wiggins and Hofman 2008; Airoldi et al. 2008), astronomy (Regier et al. 2015), and the social sciences (Erosheva, Fienberg, and Joutard 2007; Grimmer 2011).”
The Diversity–Innovation Paradox in Science (2019, background): “ at which different demographic groups relate scientific concepts in novel ways, 3 the extent to which those novel conceptual relations get taken up by other scholars, how “distal” those linkages are (14), and the subsequent returns they have to scientific careers. Our analyses use observations span”
Structural Topic Models for Open‐Ended Survey Responses (2014, background): “Rather than assume that topical prevalence (i.e., the frequency with which a topic is discussed) and topical content (i.e., the words used to discuss a topic) are constant across all participants, the analyst can incorporate covariates over which we might expect to see variance.”