113 citations · 294 across the 25 of their papers we have counts for
4 papers · 1 filter
Fast robustness quantification with variational Bayes
Ryan Giordano, Tamara Broderick, Rachael Meager +2
Bayesian hierarchical models are increasing popular in economics. When using hierarchical models, it is useful not only to calculate posterior expectations, but also to measure the…
Coresets for Scalable Bayesian Logistic Regression
Jonathan H. Huggins, Trevor Campbell, Tamara Broderick
The use of Bayesian methods in large-scale data settings is attractive because of the rich hierarchical models, uncertainty quantification, and prior specification they provide. St…
Quantifying the accuracy of approximate diffusions and Markov chains
Jonathan H. Huggins, James Zou
Markov chains and diffusion processes are indispensable tools in machine learning and statistics that are used for inference, sampling, and modeling. With the growth of large-scale…
Truncated Random Measures
Trevor Campbell, Jonathan H. Huggins, Jonathan P. How +1
Completely random measures (CRMs) and their normalizations are a rich source of Bayesian nonparametric priors. Examples include the beta, gamma, and Dirichlet processes. In this pa…