2013 - Member of the Royal Irish Academy
2009 - Member of the National Academy of Sciences
2003 - Fellow of the American Academy of Arts and Sciences
1994 - Fellow of the American Statistical Association (ASA)
His primary areas of investigation include Cluster analysis, Bayesian information criterion, Mixture model, Econometrics and Bayesian inference. His Cluster analysis study integrates concerns from other disciplines, such as Algorithm, Density estimation and Data mining. His Algorithm research is multidisciplinary, incorporating perspectives in Calculus and Statistics, Covariance.
His work carried out in the field of Econometrics brings together such families of science as Kalman filter, Probabilistic logic, Mathematical model and Markov model. The various areas that Adrian E. Raftery examines in his Bayesian inference study include State space, Selection, Markov chain, Bayes' theorem and Posterior probability. His studies deal with areas such as Mathematical optimization and Applied mathematics as well as Posterior probability.
Adrian E. Raftery focuses on Econometrics, Statistics, Bayesian inference, Bayesian probability and Artificial intelligence. His Econometrics research is multidisciplinary, incorporating elements of Fertility, Projections of population growth, Markov chain Monte Carlo, Total fertility rate and Probabilistic logic. Adrian E. Raftery has included themes like Data mining and Bayes' theorem in his Bayesian inference study.
His research in Bayesian probability focuses on subjects like Inference, which are connected to Gene regulatory network. The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Pattern recognition. His study on Cluster analysis also encompasses disciplines like
The scientist’s investigation covers issues in Econometrics, Probabilistic logic, Bayesian probability, Statistics and Life expectancy. His Econometrics study incorporates themes from Total fertility rate, Bayesian hierarchical modeling, Bayesian inference, Projections of population growth and Range. His Probabilistic forecasting study in the realm of Probabilistic logic connects with subjects such as Gravity model of trade.
His research in Bayesian probability intersects with topics in Data mining, Projection, Systems biology, Ensemble learning and Gene regulatory network. His work in Data mining addresses subjects such as Mixture model, which are connected to disciplines such as Cluster analysis. The Estimator and Prior probability research Adrian E. Raftery does as part of his general Statistics study is frequently linked to other disciplines of science, such as Respondent, therefore creating a link between diverse domains of science.
Econometrics, Bayesian hierarchical modeling, Statistics, Probabilistic logic and Projections of population growth are his primary areas of study. The study incorporates disciplines such as Tree, Life expectancy, Population size and Bayesian inference in addition to Econometrics. Bayesian inference is a primary field of his research addressed under Artificial intelligence.
His work deals with themes such as Data mapping, Per capita, Range and Markov chain Monte Carlo, which intersect with Bayesian hierarchical modeling. In general Statistics, his work in Bayesian probability, Estimator, Sampling and Resampling is often linked to Respondent linking many areas of study. The Probabilistic logic study combines topics in areas such as Prediction interval, Projection and Consensus forecast.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Bayesian Model Selection in Social Research
Adrian E. Raftery.
Sociological Methodology (1995)
Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors
Jennifer A. Hoeting;David Madigan;Adrian E. Raftery;Chris T. Volinsky.
Statistical Science (1999)
Model-Based Clustering, Discriminant Analysis, and Density Estimation
Chris Fraley;Adrian E Raftery.
Journal of the American Statistical Association (2002)
Strictly Proper Scoring Rules, Prediction, and Estimation
Tilmann Gneiting;Adrian E Raftery.
Journal of the American Statistical Association (2007)
How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
Chris Fraley;Adrian E. Raftery.
The Computer Journal (1998)
Model-based Gaussian and non-Gaussian clustering
Jeffrey D. Banfield;Adrian E. Raftery.
Biometrics (1993)
Computing Bayes Factors by Combining Simulation and Asymptotic Approximations
Thomas J. Diciccio;Robert E. Kass;Adrian Raftery;Larry Wasserman.
Journal of the American Statistical Association (1997)
Bayesian Model Averaging for Linear Regression Models
Adrian E. Raftery;David Madigan;Jennifer A. Hoeting.
Journal of the American Statistical Association (1997)
Latent Space Approaches to Social Network Analysis
Peter D Hoff;Adrian E Raftery;Mark S Handcock.
Journal of the American Statistical Association (2002)
Using Bayesian Model Averaging to Calibrate Forecast Ensembles
Adrian E. Raftery;Tilmann Gneiting;Fadoua Balabdaoui;Michael Polakowski.
Monthly Weather Review (2005)
Profile was last updated on December 6th, 2021.
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