What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Machine learning
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.
His most cited work include:
- Bayesian Model Selection in Social Research (4539 citations)
- Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors (3392 citations)
- Model-Based Clustering, Discriminant Analysis, and Density Estimation (3179 citations)
What are the main themes of his work throughout his whole career to date?
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
- Algorithm which is related to area like Model selection,
- Expectation–maximization algorithm most often made with reference to Mixture model.
He most often published in these fields:
- Econometrics (25.15%)
- Statistics (22.49%)
- Bayesian inference (18.93%)
What were the highlights of his more recent work (between 2012-2021)?
- Econometrics (25.15%)
- Probabilistic logic (12.72%)
- Bayesian probability (18.64%)
In recent papers he was focusing on the following fields of study:
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.
Between 2012 and 2021, his most popular works were:
- mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. (823 citations)
- World population stabilization unlikely this century (706 citations)
- Less than 2 °C warming by 2100 unlikely (242 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Machine learning
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.
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