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Andrew Gelman
Researcher at Columbia University
Publications - 632
Citations - 120058
Andrew Gelman is an academic researcher from Columbia University. The author has contributed to research in topics: Bayesian inference & Bayesian probability. The author has an hindex of 105, co-authored 592 publications receiving 103457 citations. Previous affiliations of Andrew Gelman include Alcatel-Lucent & University of California, Irvine.
Papers
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Book
Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Journal ArticleDOI
Inference from Iterative Simulation Using Multiple Sequences
Andrew Gelman,Donald B. Rubin +1 more
TL;DR: The focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normal- ity after transformations and marginalization, and the results are derived as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations.
Book
Data Analysis Using Regression and Multilevel/Hierarchical Models
Andrew Gelman,Yu-Sung Su +1 more
TL;DR: Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models.
Journal ArticleDOI
General methods for monitoring convergence of iterative simulations
Stephen P. Brooks,Andrew Gelman +1 more
TL;DR: This work generalizes the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances of multiple chains, in order to obtain a family of tests for convergence.
Journal ArticleDOI
Stan : A Probabilistic Programming Language
Bob Carpenter,Andrew Gelman,Matthew D. Hoffman,Daniel D. Lee,Ben Goodrich,Michael Betancourt,Marcus A. Brubaker,Jiqiang Guo,Peter Li,Allen Riddell +9 more
TL;DR: Stan as discussed by the authors is a probabilistic programming language for specifying statistical models, where a program imperatively defines a log probability function over parameters conditioned on specified data and constants, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration.