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Andrew Gelman
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- affiliation: University of Columbia, New York, USA
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2020 – today
- 2024
- [j25]Andrew Gelman
, Keith O'Rourke:
Statistics as a Social Activity: Attitudes toward Amalgamating Evidence. Entropy 26(8): 652 (2024) - [j24]Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry:
Pareto Smoothed Importance Sampling. J. Mach. Learn. Res. 25: 72:1-72:58 (2024) - 2023
- [j23]Thomas Ward
Bayesian spatial modelling of localised SARS-CoV-2 transmission through mobility networks across England. PLoS Comput. Biol. 19(11) (2023) - [j22]Anna L. Smith
Prediction scoring of data-driven discoveries for reproducible research. Stat. Comput. 33(1): 11 (2023) - [c7]Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, Eric P. Xing:
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. ICLR 2023 - [i13]Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, Eric P. Xing:
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. CoRR abs/2302.04228 (2023) - [i12]Jessica Hullman, Ari Holtzman, Andrew Gelman:
Artificial Intelligence and Aesthetic Judgment. CoRR abs/2309.12338 (2023) - 2022
- [j21]Philip Greengard
A fast regression via SVD and marginalization. Comput. Stat. 37(2): 701-720 (2022) - [j20]Yuling Yao, Aki Vehtari, Andrew Gelman:
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors. J. Mach. Learn. Res. 23: 79:1-79:45 (2022) - [j19]Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari:
Pathfinder: Parallel quasi-Newton variational inference. J. Mach. Learn. Res. 23: 306:1-306:49 (2022) - [c6]Jessica Hullman, Sayash Kapoor, Priyanka Nanayakkara, Andrew Gelman
The Worst of Both Worlds: A Comparative Analysis of Errors in Learning from Data in Psychology and Machine Learning. AIES 2022: 335-348 - [i11]Jessica Hullman, Sayash Kapoor, Priyanka Nanayakkara, Andrew Gelman, Arvind Narayanan:
The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning. CoRR abs/2203.06498 (2022) - [i10]Gwynn Sturdevant, A. Jonathan R. Godfrey, Andrew Gelman:
Delivering data differently. CoRR abs/2204.10854 (2022) - 2021
- [j18]Jon Zelner, Julien Riou
Accounting for uncertainty during a pandemic. Patterns 2(8): 100310 (2021) - [j17]Darren B. Glass, Béla Bajnok, Carolyn Yackel, Edray Goins, Jenna P. Carpenter, Stephen Kennedy, Jeanette Shakalli, Ursula Whitcher, Dave Kung, Kathryn Kozak, Robert Ghrist, Andrew Gelman:
Reviews. Am. Math. Mon. 128(2): 187-192 (2021) - [i9]Yuling Yao, Gregor Pirs
Bayesian hierarchical stacking. CoRR abs/2101.08954 (2021) - [i8]Jessica Hullman, Andrew Gelman:
To design interfaces for exploratory data analysis, we need theories of graphical inference. CoRR abs/2104.02015 (2021) - [i7]Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari:
Pathfinder: Parallel quasi-Newton variational inference. CoRR abs/2108.03782 (2021) - [i6]Tamara Broderick, Andrew Gelman, Rachael Meager, Anna L. Smith, Tian Zheng:
Toward a Taxonomy of Trust for Probabilistic Machine Learning. CoRR abs/2112.03270 (2021) - 2020
- [j16]Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian P. Robert:
Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data. J. Mach. Learn. Res. 21: 17:1-17:53 (2020) - [i5]Philip Greengard, Andrew Gelman, Aki Vehtari:
A Fast Linear Regression via SVD and Marginalization. CoRR abs/2011.04829 (2020)
2010 – 2019
- 2018
- [c5]Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman:
Yes, but Did It Work?: Evaluating Variational Inference. ICML 2018: 5577-5586 - 2017
- [j15]Andrew Gelman
The Prior Can Often Only Be Understood in the Context of the Likelihood. Entropy 19(10): 555 (2017) - [j14]Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei:
Automatic Differentiation Variational Inference. J. Mach. Learn. Res. 18: 14:1-14:45 (2017) - [j13]Aki Vehtari
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27(5): 1413-1432 (2017) - [j12]Aki Vehtari
Erratum to: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27(5): 1433 (2017) - [c4]Shravan Vasishth, Andrew Gelman:
The statistical significance filter leads to overconfident expectations of replicability. CogSci 2017 - [c3]Matilde Trevisani
A Safe Depth Forecasting Model for Insuring Tubewell Installations Against Arsenic Risk in Bangladesh. ICCSA (5) 2017: 3-19 - 2016
- [p1]Phillip N. Price, Andrew Gelman:
Centralized Analysis of Local Data, with Dollars and Lives on the Line: Lessons from the Home Radon Experience. Computational Social Science 2016: 295-306 - [i4]Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei:
Automatic Differentiation Variational Inference. CoRR abs/1603.00788 (2016) - 2015
- [j11]Ying Liu, Andrew Gelman, Tian Zheng:
Simulation-efficient shortest probability intervals. Stat. Comput. 25(4): 809-819 (2015) - [c2]Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David M. Blei:
Automatic Variational Inference in Stan. NIPS 2015: 568-576 - 2014
- [j10]Matthew D. Hoffman, Andrew Gelman:
The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res. 15(1): 1593-1623 (2014) - [j9]Andrew Gelman
Understanding predictive information criteria for Bayesian models. Stat. Comput. 24(6): 997-1016 (2014) - [i3]David Rothschild, Sharad Goel, Andrew Gelman, Doug Rivers:
The Mythical Swing Voter. CoRR abs/1406.7581 (2014) - 2012
- [i2]Nicolas Chopin, Andrew Gelman, Kerrie L. Mengersen, Christian P. Robert:
In praise of the referee. CoRR abs/1205.4304 (2012) - 2011
- [i1]Matthew D. Hoffman, Andrew Gelman:
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. CoRR abs/1111.4246 (2011)
2000 – 2009
- 2009
- [j8]Andrew Gelman:
Commentary - Discussion of the Article "Website Morphing". Mark. Sci. 28(2): 226 (2009) - 2007
- [j7]Jouni Kerman, Andrew Gelman:
Manipulating and summarizing posterior simulations using random variable objects. Stat. Comput. 17(3): 235-244 (2007) - [j6]Cavan Reilly, Andrew Gelman:
Weighted Classical Variogram Estimation for Data With Clustering. Technometrics 49(2): 184-194 (2007) - 2006
- [j5]Alessandra Casella, Andrew Gelman, Thomas R. Palfrey:
An experimental study of storable votes. Games Econ. Behav. 57(1): 123-154 (2006) - [j4]Andrew Gelman
Bayesian Measures of Explained Variance and Pooling in Multilevel (Hierarchical) Models. Technometrics 48(2): 241-251 (2006) - [j3]Andrew Gelman:
Multilevel (Hierarchical) Modeling: What It Can and Cannot Do. Technometrics 48(3): 432-435 (2006) - 2000
- [j2]Iwin Leenen, Iven Van Mechelen, Andrew Gelman:
Bayesian probabilistic extensions of a deterministic classification model. Comput. Stat. 15(3): 355-371 (2000) - [j1]Andrew Gelman
Type S error rates for classical and Bayesian single and multiple comparison procedures. Comput. Stat. 15(3): 373-390 (2000)
1980 – 1989
- 1988
- [c1]Andrew Gelman, Susan Altman, Matt Pallakoff, Ketan Doshi, Catherine Manago, Thomas C. Rindfleisch, Bruce G. Buchanan:
FRM: An Intelligent Assistant for Financial Resource Management. AAAI 1988: 31-36
Coauthor Index
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last updated on 2024-10-23 20:25 CEST by the dblp team
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