
Automatic Differentiation Variational Inference
Probabilistic modeling is iterative. A scientist posits a simple model, ...
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Automatic Variational Inference in Stan
Variational inference is a scalable technique for approximate Bayesian i...
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Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
A common approach for Bayesian computation with big data is to partition...
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Bayesian hierarchical weighting adjustment and survey inference
We combine Bayesian prediction and weighted inference as a unified appro...
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Voting patterns in 2016: Exploration using multilevel regression and poststratification (MRP) on preelection polls
We analyzed 2012 and 2016 YouGov preelection polls in order to understa...
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Bayesian Inference under Cluster Sampling with Probability Proportional to Size
Cluster sampling is common in survey practice, and the corresponding inf...
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Validating Bayesian Inference Algorithms with SimulationBased Calibration
Verifying the correctness of Bayesian computation is challenging. This i...
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Yes, but Did It Work?: Evaluating Variational Inference
While it's always possible to compute a variational approximation to a p...
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Limitations of "Limitations of Bayesian leaveoneout crossvalidation for model selection"
This article is an invited discussion of the article by Gronau and Wagen...
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The Political Significance of Social Penumbras
To explain the political clout of different social groups, traditional a...
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The experiment is just as important as the likelihood in understanding the prior: A cautionary note on robust cognitive modelling
Cognitive modelling shares many features with statistical modelling, mak...
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Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data
It is not always clear how to adjust for control data in causal inferenc...
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Ranknormalization, folding, and localization: An improved R for assessing convergence of MCMC
Markov chain Monte Carlo is a key computational tool in Bayesian statist...
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Know your population and know your model: Using modelbased regression and poststratification to generalize findings beyond the observed sample
Psychology is all about interactions, and this has deep implications for...
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Improving multilevel regression and poststratification with structured priors
A central theme in the field of survey statistics is estimating populati...
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Holes in Bayesian Statistics
Every philosophy has holes, and it is the responsibility of proponents o...
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Andrew Gelman
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Andrew Gelman is an American statistics professor and director of the Columbia University’s Applied Statistics Center. He got a S.B. He received three Outstanding Statistical Application Award from the American statistical association in mathematics and physics from MIT in 1986 and a PhD in statistics from Harvard University in 1990 under the supervision of Donald Rubin. He is an elected fellow of the American Statistical Association and the Mathematical Statistical Institute.
In 2002, Gelman married and has three children, Caroline Rosenthal. The psychologist Susan Gelman is his older sister. Woody Gelman, the cartoonist, was his uncle.
At present, Gelman is a political science and statistics professor at the University of Columbia, where he also heads the Applied Statistical Center. The Applied Statistics Center conducts research at Columbia University and includes a number of individual projects with several other departments. Gelmann is a Bayesian statistics practitioner and hierarchical models. He contributes greatly to Stan’s framework of statistical programming.
Gelman is noteworthy for his efforts to make journalists and the public more accessible to political science and statistics. He is one of the main authors of The Monkey Cage, a blog from the Washington Post dedicated to providing informed commentary on political science, and making political science accessible. He often writes about Bayesian statistics, displays data and interesting social science trends. According to the New York Times, on the blog “he postes his thoughts on good statistical practices in the sciences, frequently emphasizing what he sees as the absurd or the unscientific… he is sufficiently respected that his posts are read well; he cuts down sufficiently that many of his critics have a strong sense of joy.”
Andrew Gelman, David Park, Jeronimo Cortina, Boris Shor. “Red State, Blue State, Rich State, Poor State: Why Americans Vote The Way You Do.”
Jennifer Hill and Andrew Gelman. “Regression data analysis and multilevel/hierarchical models.” University Press, Cambridge, 2006. ISBN 9780521686891 ISBN 9780
Andrew Gelman and Nolan Deborah. “The Statistics of Teaching: A Bag of Tricks.” University Press, Oxford, 2002. ISBN 9780 19857233 ISBN
Andrew Gelman, Hal S. Stern, John B. Carlin, David Dunson, Aki Vehtari and Donald B. Rubin. “Analysis of Bayesian Data” CRC/Chapman & Hall, 2013.