Verifying the correctness of Bayesian computation is challenging. This is
especially true for complex models that are common in practice, as these
require sophisticated model implementations and algorithms. In this paper we
introduce simulation-based calibration (SBC), a general procedure for
validating inferences from Bayesian algorithms capable of generating posterior
samples. This procedure not only identifies inaccurate computation and
inconsistencies in model implementations but also provides graphical summaries
that can indicate the nature of the problems that arise. We argue that SBC is a
critical part of a robust Bayesian workflow, as well as being a useful tool for
those developing computational algorithms and statistical software.
Описание
[1804.06788] Validating Bayesian Inference Algorithms with Simulation-Based Calibration
%0 Journal Article
%1 talts2018validating
%A Talts, Sean
%A Betancourt, Michael
%A Simpson, Daniel
%A Vehtari, Aki
%A Gelman, Andrew
%D 2018
%K bayesian calibration
%T Validating Bayesian Inference Algorithms with Simulation-Based
Calibration
%U http://arxiv.org/abs/1804.06788
%X Verifying the correctness of Bayesian computation is challenging. This is
especially true for complex models that are common in practice, as these
require sophisticated model implementations and algorithms. In this paper we
introduce simulation-based calibration (SBC), a general procedure for
validating inferences from Bayesian algorithms capable of generating posterior
samples. This procedure not only identifies inaccurate computation and
inconsistencies in model implementations but also provides graphical summaries
that can indicate the nature of the problems that arise. We argue that SBC is a
critical part of a robust Bayesian workflow, as well as being a useful tool for
those developing computational algorithms and statistical software.
@article{talts2018validating,
abstract = {Verifying the correctness of Bayesian computation is challenging. This is
especially true for complex models that are common in practice, as these
require sophisticated model implementations and algorithms. In this paper we
introduce \emph{simulation-based calibration} (SBC), a general procedure for
validating inferences from Bayesian algorithms capable of generating posterior
samples. This procedure not only identifies inaccurate computation and
inconsistencies in model implementations but also provides graphical summaries
that can indicate the nature of the problems that arise. We argue that SBC is a
critical part of a robust Bayesian workflow, as well as being a useful tool for
those developing computational algorithms and statistical software.},
added-at = {2019-07-20T20:35:04.000+0200},
author = {Talts, Sean and Betancourt, Michael and Simpson, Daniel and Vehtari, Aki and Gelman, Andrew},
biburl = {https://www.bibsonomy.org/bibtex/2a14fc337937ea341566ccd926464a1a2/kirk86},
description = {[1804.06788] Validating Bayesian Inference Algorithms with Simulation-Based Calibration},
interhash = {5304d7360f92bff961e8c775d80b5fe3},
intrahash = {a14fc337937ea341566ccd926464a1a2},
keywords = {bayesian calibration},
note = {cite arxiv:1804.06788Comment: 26 pages, 14 figures},
timestamp = {2019-07-20T20:35:04.000+0200},
title = {Validating Bayesian Inference Algorithms with Simulation-Based
Calibration},
url = {http://arxiv.org/abs/1804.06788},
year = 2018
}