Automatic Differentiation Variational Inference. Kucukelbir, A, Tran, D, Ranganath, R, Gelman, A, & Blei, D M faculty.chicagobooth.edu. abstract bibtex Abstract 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 large data is a bottleneck in this process. Deriving algorithms to fit new models can be.
@Article{Kucukelbir,
author = {Kucukelbir, A and Tran, D and Ranganath, R and Gelman, A and Blei, D M},
title = {Automatic Differentiation Variational Inference},
journal = {faculty.chicagobooth.edu},
volume = {},
number = {},
pages = {},
year = {},
abstract = {Abstract 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 large data is a bottleneck in this process. Deriving algorithms to fit new models can be.},
location = {},
keywords = {}}
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