Bayesian data analysis, 3rd edition A Gelman, JB Carlin, HS Stern, DB Dunson, A Vehtari, DB Rubin Chapman & Hall/CRC, 2013 | 28607* | 2013 |

Data analysis using regression and multilevel/hierarchical models A Gelman, J Hill Cambridge university press, 2006 | 13268 | 2006 |

Inference from iterative simulation using multiple sequences A Gelman, DB Rubin Statistical science 7 (4), 457-472, 1992 | 12672 | 1992 |

General methods for monitoring convergence of iterative simulations SP Brooks, A Gelman Journal of computational and graphical statistics 7 (4), 434-455, 1998 | 5402 | 1998 |

Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) A Gelman Bayesian analysis 1 (3), 515-534, 2006 | 3922 | 2006 |

Stan: A probabilistic programming language B Carpenter, A Gelman, MD Hoffman, D Lee, B Goodrich, M Betancourt, ... Journal of statistical software 76 (1), 2017 | 3274 | 2017 |

Posterior predictive assessment of model fitness via realized discrepancies A Gelman, XL Meng, H Stern Statistica sinica, 733-760, 1996 | 2260 | 1996 |

The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. MD Hoffman, A Gelman J. Mach. Learn. Res. 15 (1), 1593-1623, 2014 | 2101 | 2014 |

Handbook of markov chain monte carlo S Brooks, A Gelman, G Jones, XL Meng CRC press, 2011 | 1795 | 2011 |

Weak convergence and optimal scaling of random walk Metropolis algorithms GO Roberts, A Gelman, WR Gilks The annals of applied probability 7 (1), 110-120, 1997 | 1766 | 1997 |

R2WinBUGS: a package for running WinBUGS from R S Sturtz, U Ligges, AE Gelman | 1660 | 2005 |

Scaling regression inputs by dividing by two standard deviations A Gelman Statistics in medicine 27 (15), 2865-2873, 2008 | 1553 | 2008 |

A weakly informative default prior distribution for logistic and other regression models A Gelman, A Jakulin, MG Pittau, YS Su The annals of applied statistics 2 (4), 1360-1383, 2008 | 1448 | 2008 |

Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC A Vehtari, A Gelman, J Gabry Statistics and computing 27 (5), 1413-1432, 2017 | 1429 | 2017 |

Efficient Metropolis jumping rules A Gelman, GO Roberts, WR Gilks Bayesian statistics 5 (599-608), 42, 1996 | 1289 | 1996 |

Why are American presidential election campaign polls so variable when votes are so predictable? A Gelman, G King British Journal of Political Science, 409-451, 1993 | 1093 | 1993 |

Understanding predictive information criteria for Bayesian models A Gelman, J Hwang, A Vehtari Statistics and computing 24 (6), 997-1016, 2014 | 1082 | 2014 |

Simulating normalizing constants: From importance sampling to bridge sampling to path sampling A Gelman, XL Meng Statistical science, 163-185, 1998 | 1055 | 1998 |

Why high-order polynomials should not be used in regression discontinuity designs A Gelman, G Imbens Journal of Business & Economic Statistics 37 (3), 447-456, 2019 | 997 | 2019 |

The difference between “significant” and “not significant” is not itself statistically significant A Gelman, H Stern The American Statistician 60 (4), 328-331, 2006 | 897 | 2006 |