% Encoding: UTF-8

@Misc{SDT2018,
  author = {{Stan Development Team}},
  title  = {{RStan}: the {R} interface to {Stan}},
  year   = {2018},
  note   = {R package version 2.18.1},
  url    = {http://mc-stan.org/},
}

@Manual{Team2017,
  title  = {Stan Modeling Language: User’s Guideand Reference Manual. Version 2.17.0.},
  author = {Stan Development Team},
  year   = {2017},
  url    = {http://mc-stan.org/users/documentation/},
}

@Article{Carpenter2017,
  author    = {Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew D. and Lee, Daniel and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus and Guo, Jiqiang and Li, Peter and Riddell, Allen},
  title     = {Stan: A Probabilistic Programming Language},
  journal   = {Journal of Statistical Software},
  year      = {2017},
  volume    = {76},
  number    = {1},
  doi       = {10.18637/jss.v076.i01},
  publisher = {Foundation for Open Access Statistic},
  url       = {http://dx.doi.org/10.18637/jss.v076.i01},
}

@Unpublished{Betancourt2018,
  author = {Michael Betancourt},
  title  = {Towards A Principled Bayesian Workflow},
  month  = {October},
  year   = {2018},
  url    = {https://betanalpha.github.io/assets/case_studies/principled_bayesian_workflow.html},
}

@Article{Gabry2017,
  author      = {Jonah Gabry and Daniel Simpson and Aki Vehtari and Michael Betancourt and Andrew Gelman},
  title       = {Visualization in Bayesian workflow},
  abstract    = {Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high-dimensional models that are used by applied researchers.},
  date        = {2017-09-05},
  eprint      = {1709.01449v5},
  eprintclass = {stat.ME},
  eprinttype  = {arXiv},
  file        = {online:http\://arxiv.org/pdf/1709.01449v5:PDF},
  keywords    = {stat.ME, stat.AP},
}

@Article{Talts2018,
  author      = {Sean Talts and Michael Betancourt and Daniel Simpson and Aki Vehtari and Andrew Gelman},
  title       = {Validating Bayesian Inference Algorithms with Simulation-Based Calibration},
  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.},
  date        = {2018-04-18},
  eprint      = {1804.06788v1},
  eprintclass = {stat.ME},
  eprinttype  = {arXiv},
  file        = {online:http\://arxiv.org/pdf/1804.06788v1:PDF},
  keywords    = {stat.ME},
}

@Article{Cook2006,
  author    = {Samantha R Cook and Andrew Gelman and Donald B Rubin},
  title     = {Validation of Software for Bayesian Models Using Posterior Quantiles},
  journal   = {Journal of Computational and Graphical Statistics},
  year      = {2006},
  volume    = {15},
  number    = {3},
  pages     = {675--692},
  month     = {sep},
  doi       = {10.1198/106186006x136976},
  publisher = {Informa {UK} Limited},
}

@Misc{gabry2018,
  author = {Gabry, Jonah and Mahr, Tristan and Bürkner, Paul-Christian and Modrák, Martin and Barrett, Malcolm},
  title  = {{bayesplot}: Plotting for Bayesian Models},
  year   = {2018},
  note   = {R package version 1.6.1},
  url    = {https://CRAN.R-project.org/package=bayesplot},
}

@Book{Gelman2013,
  title     = {Bayesian Data Analysis},
  publisher = {Taylor \& Francis Ltd},
  year      = {2013},
  author    = {Gelman, Andrew and Carlin, John B. and Stern, Hal S. and Dunson, David B. and Vehtari, Aki and Rubin, Donald B.},
  isbn      = {1439840954},
  date      = {2013-11-01},
  ean       = {9781439840955},
  pagetotal = {675},
}

@Article{Damiano2018,
  author    = {Damiano, Luis and Peterson, Brian and Weylandt, Michael},
  title     = {A Tutorial On Hidden Markov Models Using Stan},
  year      = {2018},
  doi       = {10.5281/zenodo.1284341},
  keywords  = {StanCon, Stan, Bayesian Data Analysis},
  publisher = {Zenodo},
}

@Comment{jabref-meta: databaseType:bibtex;}
