Παρακολούθηση
Andrew Gelman
Andrew Gelman
Professor of Statistics and Political Science, Columbia University
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα stat.columbia.edu - Αρχική σελίδα
Τίτλος
Παρατίθεται από
Παρατίθεται από
Έτος
Bayesian data analysis, 3rd edition
A Gelman, JB Carlin, HS Stern, DB Dunson, A Vehtari, DB Rubin
Chapman & Hall/CRC, 2013
38462*2013
Data analysis using regression and multilevel/hierarchical models
A Gelman, J Hill
Cambridge university press, 2006
186402006
Inference from iterative simulation using multiple sequences
A Gelman, DB Rubin
Statistical science 7 (4), 457-472, 1992
182731992
General methods for monitoring convergence of iterative simulations
SP Brooks, A Gelman
Journal of computational and graphical statistics 7 (4), 434-455, 1998
76841998
Stan: A probabilistic programming language
B Carpenter, A Gelman, MD Hoffman, D Lee, B Goodrich, M Betancourt, ...
Journal of statistical software 76, 2017
74252017
Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)
A Gelman
53772006
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
50962014
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
A Vehtari, A Gelman, J Gabry
Statistics and computing 27, 1413-1432, 2017
44542017
Handbook of markov chain monte carlo
S Brooks, A Gelman, G Jones, XL Meng
CRC press, 2011
32702011
Posterior predictive assessment of model fitness via realized discrepancies
A Gelman, XL Meng, H Stern
Statistica sinica, 733-760, 1996
29521996
Scaling regression inputs by dividing by two standard deviations
A Gelman
Statistics in medicine 27 (15), 2865-2873, 2008
24682008
Weak convergence and optimal scaling of random walk Metropolis algorithms
A Gelman, WR Gilks, GO Roberts
The annals of applied probability 7 (1), 110-120, 1997
23381997
A weakly informative default prior distribution for logistic and other regression models
A Gelman, A Jakulin, MG Pittau, YS Su
22342008
Understanding predictive information criteria for Bayesian models
A Gelman, J Hwang, A Vehtari
Statistics and computing 24, 997-1016, 2014
21432014
R2WinBUGS: a package for running WinBUGS from R
S Sturtz, U Ligges, A Gelman
Journal of Statistical software 12, 1-16, 2005
20302005
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
19712019
Efficient Metropolis jumping rules
A Gelman, GO Roberts, WR Gilks
Bayesian statistics 5 5, 599-608, 1996
15681996
Why we (usually) don't have to worry about multiple comparisons
A Gelman, J Hill, M Yajima
Journal of research on educational effectiveness 5 (2), 189-211, 2012
14262012
Beyond power calculations: Assessing type S (sign) and type M (magnitude) errors
A Gelman, J Carlin
Perspectives on Psychological Science 9 (6), 641-651, 2014
13312014
Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
A Gelman, XL Meng
Statistical science, 163-185, 1998
12811998
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