Andrew Gelman
Andrew Gelman
Professor of Statistics and Political Science, Columbia University
Verified email at - Homepage
Cited by
Cited by
Bayesian data analysis, 3rd edition
A Gelman, JB Carlin, HS Stern, DB Dunson, A Vehtari, DB Rubin
Chapman & Hall/CRC, 2013
Data analysis using regression and multilevel/hierarchical models
A Gelman, J Hill
Cambridge university press, 2006
Inference from iterative simulation using multiple sequences
A Gelman, DB Rubin
Statistical science 7 (4), 457-472, 1992
General methods for monitoring convergence of iterative simulations
SP Brooks, A Gelman
Journal of computational and graphical statistics 7 (4), 434-455, 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
Stan: a probabilistic programming language.
B Carpenter, A Gelman, MD Hoffman, D Lee, B Goodrich, M Betancourt, ...
Grantee Submission 76 (1), 1-32, 2017
Posterior predictive assessment of model fitness via realized discrepancies
A Gelman, XL Meng, H Stern
Statistica sinica, 733-760, 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
Handbook of markov chain monte carlo
S Brooks, A Gelman, G Jones, XL Meng
CRC press, 2011
Weak convergence and optimal scaling of random walk Metropolis algorithms
GO Roberts, A Gelman, WR Gilks
Annals of Applied probability 7 (1), 110-120, 1997
R2WinBUGS: a package for running WinBUGS from R
S Sturtz, U Ligges, AE Gelman
Scaling regression inputs by dividing by two standard deviations
A Gelman
Statistics in medicine 27 (15), 2865-2873, 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
A weakly informative default prior distribution for logistic and other regression models
A Gelman, A Jakulin, MG Pittau, YS Su
Annals of applied Statistics 2 (4), 1360-1383, 2008
Efficient Metropolis jumping rules
A Gelman, GO Roberts, WR Gilks
Bayesian statistics 5 (599-608), 42, 1996
Understanding predictive information criteria for Bayesian models
A Gelman, J Hwang, A Vehtari
Statistics and computing 24 (6), 997-1016, 2014
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
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
Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
A Gelman, XL Meng
Statistical science, 163-185, 1998
The difference between “significant” and “not significant” is not itself statistically significant
A Gelman, H Stern
The American Statistician 60 (4), 328-331, 2006
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