What is he best known for?
The fields of study he is best known for:
- Statistics
- Law
- Normal distribution
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.
His most cited work include:
- Bayesian Data Analysis (13605 citations)
- Inference from Iterative Simulation Using Multiple Sequences (9806 citations)
- Data Analysis Using Regression and Multilevel/Hierarchical Models (7199 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Statistics (20.33%)
- Econometrics (17.77%)
- Bayesian probability (16.11%)
What were the highlights of his more recent work (between 2017-2021)?
- Bayesian probability (16.11%)
- Inference (7.83%)
- Artificial intelligence (9.64%)
In recent papers he was focusing on the following fields of study:
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.
Between 2017 and 2021, his most popular works were:
- Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs (294 citations)
- Abandon Statistical Significance (241 citations)
- Visualization in Bayesian workflow (176 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Law
- Normal distribution
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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