hxml.bib

% This is hxml.bib, the shared, annotated bibtex file of the SLAC HXML group. 
%
% Copyright 2019 SLAC National Accelerator Laboratory, and freely available for re-use under the BSD-3 license.
% Contributors (alphabetical): Michael Kagan, Phil Marshall, Ji Won Park, Sebastian Wagner-Carena
% Comments/questions welcome at https://github.com/drphilmarshall/BreakfastClub/issues
%
% Topic: Hierarchical Models

@ARTICLE{TRB2017,
       author = {{Tran}, Dustin and {Ranganath}, Rajesh and {Blei}, David M.},
        title = "{Hierarchical Implicit Models and Likelihood-Free Variational Inference}",
      journal = {arXiv e-prints},
     keywords = {Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Computation, Statistics - Methodology},
         year = "2017",
        month = "Feb",
          eid = {arXiv:1702.08896},
        pages = {arXiv:1702.08896},
archivePrefix = {arXiv},
       eprint = {1702.08896},
 primaryClass = {stat.ML},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2017arXiv170208896T},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System},
         note = "{Discussed 2020-01-28. This paper suggests getting around the need for an explicit pdf in variational inference by using ratio estimation. The ratio estimation is deterministic, and the posterior samples are a deterministic process with noisy inputs. This work is explicit about the global variables being used (akin to the global cosmological parameters in our case).}"
}