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Gelman, Andrew

  1. Bayesian Workflow
    2020/11/03 by Andrew Gelman, Gelman, Andrew, Aki Vehtari +18 · 5 voices · 30 citations
    Computer Science · Decision Sciences · #Bayesian Modeling and Causal Inference #Scientific Computing and Data Management #Data Quality and Management
  2. The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
    2011/11/18 by Matthew D. Hoffman, Hoffman, Matthew D., Andrew Gelman +1 · 159 citations
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Markov Chains and Monte Carlo Methods
  3. Validating Bayesian Inference Algorithms with Simulation-Based Calibration
    2018/04/18 by Sean Talts, Talts, Sean, Michael Betancourt +7 · 2 voices · 34 citations
    Computer Science · Mathematics · #Gaussian Processes and Bayesian Inference #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #stat.ME
  4. Why we (usually) don't have to worry about multiple comparisons
    2009/07/15 by Andrew Gelman, Jennifer Hill, Gelman, Andrew +3 · 2 voices · 10 citations
    #stat.AP #stat.ME
  5. Automatic Differentiation Variational Inference
    2016/03/02 by Alp Kucukelbir, Kucukelbir, Alp, Dustin Tran +7 · 24 citations
    Computer Science · #Gaussian Processes and Bayesian Inference #Machine Learning and Algorithms #Machine Learning and Data Classification
  6. Understanding predictive information criteria for Bayesian models
    2013/07/23 by Andrew Gelman, Jessica Hwang, Gelman, Andrew +3 · 17 citations
    Mathematics · Decision Sciences · #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Forecasting Techniques and Applications
  7. Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
    2014/12/16 by Aki Vehtari, Vehtari, Aki, Andrew Gelman +17 · 5 citations
    Computer Science · #Gaussian Processes and Bayesian Inference #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference
  8. Automatic Variational Inference in Stan
    2015/06/10 by Alp Kucukelbir, Rajesh Ranganath, Kucukelbir, Alp +5 · 6 citations
    Computer Science · #Bayesian Methods and Mixture Models #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML)
  9. Pathfinder: Parallel quasi-Newton variational inference
    2021/08/09 by Lu Zhang, Bob Carpenter, Zhang, Lu +5 · 7 citations
    Computer Science · Decision Sciences · Mathematics · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Probabilistic and Robust Engineering Design #Statistical Methods and Inference
  10. Bayesian hierarchical weighting adjustment and survey inference
    2017/07/25 by Si, Yajuan, Trangucci, Rob, Gabry, Jonah Sol +1 · 3 citations
    #Applications (stat.AP) #Computation (stat.CO) #FOS: Computer and information sciences #Methodology (stat.ME)
  11. On the Stationary Distribution of Iterative Imputations
    2010/12/13 by Jingchen Liu, Andrew Gelman, Liu, Jingchen +5 · 2 citations
    Computer Science · Mathematics · #62D05 #62E20 #Bayesian Methods and Mixture Models #FOS: Mathematics #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics Theory (math.ST)
  12. To design interfaces for exploratory data analysis, we need theories of graphical inference
    2021/04/05 by Hullman, Jessica, Gelman, Andrew · 3 citations
    #FOS: Computer and information sciences #H.5.2 #Human-Computer Interaction (cs.HC)
  13. Beyond subjective and objective in statistics
    2015/08/22 by Andrew Gelman, Gelman, Andrew, Christian Hennig +1 · 2 citations
    Arts and Humanities · #Applications (stat.AP) #FOS: Computer and information sciences #Other Statistics (stat.OT) #Philosophy and History of Science
  14. Causal quartets: Different ways to attain the same average treatment effect
    2023/02/24 by Andrew Gelman, Gelman, Andrew, Jessica Hullman +3 · 3 citations
    Computer Science · Mathematics · #Advanced Causal Inference Techniques #Applications (stat.AP) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Methodology (stat.ME)
  15. Causality and Statistical Learning
    2010/03/12 by Andrew Gelman, Gelman, Andrew · 1 citation
    Computer Science · Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Qualitative Comparative Analysis Research #Statistics Theory (math.ST)
  16. Conformal Prediction and Human Decision Making
    2025/03/12 by Jessica Hullman, Yifan Wu, Hullman, Jessica +7 · 1 voice · 4 citations
    #cs.LG #cs.AI #stat.ML
  17. A Model-Based Approach to Climate Reconstruction Using Tree-Ring Data
    2015/01/01 by Matthew Schofield, Schofield, Matthew R., Richard Barker +7 · 1 citation
    Earth and Planetary Sciences · Environmental Science · #Tree-ring climate responses #Plant Water Relations and Carbon Dynamics #Climate variability and models
  18. Russian roulette: The need for stochastic potential outcomes when utilities depend on counterfactuals
    2024/12/16 by Andrew Gelman, Jonas M. Mikhaeil, Gelman, Andrew +1 · 3 citations
    Decision Sciences · Social Sciences · #Decision-Making and Behavioral Economics #Risk Perception and Management
  19. Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data
    2019/05/21 by Gelman, Andrew, Vákár, Matthijs · 1 citation
    #Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME)
  20. Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors
    2020/06/22 by Yao, Yuling, Vehtari, Aki, Gelman, Andrew · 1 citation
    #FOS: Computer and information sciences #Machine Learning (stat.ML) #Methodology (stat.ME)
  21. A proposal for informative default priors scaled by the standard error\n of estimates
    2020/11/30 by Erik W. van Zwet, van Zwet, Erik, Andrew Gelman +1 · 1 citation
    Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Inference #Statistical Methods in Clinical Trials
  22. Accounting for Uncertainty During a Pandemic
    2020/06/15 by Zelner, Jon, Riou, Julien, Etzioni, Ruth +1 · 1 citation
    #Applications (stat.AP) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Populations and Evolution (q-bio.PE) #Quantitative Methods (q-bio.QM)
  23. What are the most important statistical ideas of the past 50 years?
    2020/11/30 by Gelman, Andrew, Vehtari, Aki · 1 citation
    #FOS: Computer and information sciences #Methodology (stat.ME)
  24. Using leave-one-out cross-validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A cautionary tale
    2022/09/05 by Swen Kuh, Lauren Kennedy, Kuh, Swen +5 · 1 citation
    Mathematics · Social Sciences · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Health disparities and outcomes #Methodology (stat.ME) #Statistical Methods and Bayesian Inference
  25. "How many zombies do you know?" Using indirect survey methods to measure alien attacks and outbreaks of the undead
    2010/03/31 by Andrew Gelman, Gelman, Andrew · 2 voices
    #physics.soc-ph #physics.data-an
  26. Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
    2023/02/08 by Guo, Han, Greengard, Philip, Wang, Hongyi +3 · 1 citation
    #FOS: Computer and information sciences #Machine Learning (cs.LG)
  27. Artificial Intelligence and Aesthetic Judgment
    2023/08/21 by Jessica Hullman, Ari Holtzman, Hullman, Jessica +3 · 1 citation
    Neuroscience · Computer Science · #Aesthetic Perception and Analysis #Generative Adversarial Networks and Image Synthesis
  28. For how many iterations should we run Markov chain Monte Carlo?
    2023/11/05 by Margossian, Charles C., Gelman, Andrew · 1 citation
    #Computation (stat.CO) #FOS: Computer and information sciences
  29. Hierarchical Bayesian Models to Mitigate Systematic Disparities in Prediction with Proxy Outcomes
    2024/03/01 by Jonas M. Mikhaeil, Andrew Gelman, Mikhaeil, Jonas +3 · 1 citation
    Computer Science · #Data Analysis with R #Machine Learning in Healthcare
  30. Routine Hospital-based SARS-CoV-2 Testing Outperforms State-based Data in Predicting Clinical Burden
    2021/04/09 by Covello, Len, Gelman, Andrew, Si, Yajuan +1 · 1 citation
    #Applications (stat.AP) #FOS: Computer and information sciences
  31. Adaptive Path Sampling in Metastable Posterior Distributions
    2020/09/01 by Yuling Yao, Collin Cademartori, Yao, Yuling +5 · 1 citation
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Markov Chains and Monte Carlo Methods #Methodology (stat.ME)
  32. Inference from Non-Random Samples Using Bayesian Machine Learning
    2021/04/12 by Yutao Liu, Andrew Gelman, Liu, Yutao +3 · 1 citation
    Mathematics · #Advanced Causal Inference Techniques #Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference
  33. The piranha problem: Large effects swimming in a small pond
    2021/05/27 by Tosh, Christopher, Greengard, Philip, Goodrich, Ben +3 · 3 voices
    #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Mathematics #Statistics Theory (math.ST)