2020 - Fellow of the American Academy of Arts and Sciences
2003 - COPSS Presidents' Award
1998 - Fellow of the American Statistical Association (ASA)
Bayesian probability, Bayesian inference, Statistics, Econometrics and Artificial intelligence are his primary areas of study. He has included themes like Data mining and Markov chain in his Bayesian probability study. His Bayesian inference research is multidisciplinary, relying on both Algorithm, Bayes' theorem, Probabilistic programming language and Gibbs sampling.
Andrew Gelman interconnects Mixture model, Inference, Multiple-try Metropolis and Markov chain Monte Carlo in the investigation of issues within Algorithm. The concepts of his Statistics study are interwoven with issues in Hierarchical database model, Sign and Pooling. His Econometrics research includes themes of Logistic regression, Simple linear regression, Survey methodology, Estimation and Multilevel model.
Andrew Gelman focuses on Statistics, Econometrics, Bayesian probability, Bayesian inference and Artificial intelligence. His work in Multilevel model, Regression analysis, Marginal model, Linear regression and Regression are all subfields of Statistics research. Andrew Gelman performs integrative study on Econometrics and Context.
The various areas that Andrew Gelman examines in his Bayesian probability study include Inference and Data mining. His Bayesian inference study integrates concerns from other disciplines, such as Algorithm and Prior probability. His study connects Machine learning and Artificial intelligence.
Andrew Gelman spends much of his time researching Bayesian probability, Inference, Artificial intelligence, Bayesian inference and Machine learning. His Bayesian probability study is concerned with the field of Statistics as a whole. His Inference study combines topics in areas such as Importance sampling, Econometrics, Algorithm, Posterior probability and Sample.
His study looks at the relationship between Artificial intelligence and topics such as Causal inference, which overlap with Bayesian hierarchical modeling and Variance. He carries out multidisciplinary research, doing studies in Bayesian inference and Context. His Machine learning course of study focuses on Bayesian statistics and Prior probability, Advice, Mathematical economics and Workflow.
His main research concerns Bayesian probability, Statistics, Inference, Bayesian inference and Algorithm. Andrew Gelman has researched Bayesian probability in several fields, including Cross-validation, Data mining and Census. His studies in Data mining integrate themes in fields like Model checking, Statistical graphics and Markov chain.
His research investigates the connection between Inference and topics such as Posterior probability that intersect with problems in Visualization and Workflow. The study incorporates disciplines such as Prevalence, Sample, Machine learning and Selection bias in addition to Bayesian inference. His studies deal with areas such as Calibration, Normalization, Importance sampling and Markov chain Monte Carlo as well as Algorithm.
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Bayesian Data Analysis
Andrew Gelman;John B. Carlin;Hal S. Stern;David B. Dunson.
Data Analysis Using Regression and Multilevel/Hierarchical Models
Andrew Gelman;Yu-Sung Su.
Inference from Iterative Simulation Using Multiple Sequences
Andrew Gelman;Donald B. Rubin.
Statistical Science (1992)
General methods for monitoring convergence of iterative simulations
Stephen P. Brooks;Andrew Gelman.
Journal of Computational and Graphical Statistics (1998)
Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)
Bayesian Analysis (2006)
Stan: A Probabilistic Programming Language
Bob Carpenter;Andrew Gelman;Matthew D. Hoffman;Daniel Lee.
Journal of Statistical Software (2017)
Prior distributions for variance parameters in hierarchical models
EERI Research Paper Series (2004)
POSTERIOR PREDICTIVE ASSESSMENT OF MODEL FITNESS VIA REALIZED DISCREPANCIES
Andrew Gelman;Xiao-Li Meng;Hal Stern.
The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
Matthew D. Homan;Andrew Gelman.
Journal of Machine Learning Research (2014)
Handbook of Markov Chain Monte Carlo
Steve Brooks;Andrew Gelman;Galin L. Jones;Xiao-Li Meng.
Profile was last updated on December 6th, 2021.
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