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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