957 citations · 1.4k across the 39 of their papers we have counts for
4 papers · 2 filters
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations
Aaron Schein, Mingyuan Zhou, David M. Blei +1
We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country--country interaction event data. These data consist of interaction events of the form "country to…
Posterior Dispersion Indices
Alp Kucukelbir, David M. Blei
Probabilistic modeling is cyclical: we specify a model, infer its posterior, and evaluate its performance. Evaluation drives the cycle, as we revise our model based on how it perfo…
Overdispersed Black-Box Variational Inference
Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei
We introduce overdispersed black-box variational inference, a method to reduce the variance of the Monte Carlo estimator of the gradient in black-box variational inference. Instead…
Automatic Differentiation Variational Inference
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath +2
Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines it according to her analysis, and repeats. However, fitting complex models to l…