What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Probability distribution
His main research concerns Statistics, Econometrics, Gaussian process, Algorithm and Uncertainty analysis.
His work deals with themes such as Expert elicitation, Mathematical optimization, Bayes classifier, Statistical model and A priori probability, which intersect with Econometrics.
His studies deal with areas such as Bayes error rate, Bayes' theorem and Sampling distribution as well as Algorithm.
His Uncertainty analysis study incorporates themes from Calibration and Computer experiment.
As part of one scientific family, Anthony O'Hagan deals mainly with the area of Computer experiment, narrowing it down to issues related to the Emulation, and often Computer engineering, Complex system and Case-based reasoning.
His research investigates the connection between Markov chain Monte Carlo and topics such as Covariance function that intersect with problems in Bayesian statistics and Posterior probability.
His most cited work include:
- Bayesian Calibration of computer models (2498 citations)
- Probabilistic sensitivity analysis of complex models: a Bayesian approach (794 citations)
- Predicting the output from a complex computer code when fast approximations are available (782 citations)
What are the main themes of his work throughout his whole career to date?
Anthony O'Hagan mainly focuses on Econometrics, Bayesian probability, Statistics, Artificial intelligence and Bayes' theorem.
His work in the fields of Econometrics, such as Nonparametric statistics, intersects with other areas such as Context.
His Bayesian probability research is multidisciplinary, relying on both Clinical trial and Data mining.
Anthony O'Hagan incorporates Statistics and Gaussian process in his studies.
In his research, Uncertainty analysis is intimately related to Machine learning, which falls under the overarching field of Artificial intelligence.
His Bayes' theorem research focuses on subjects like Bayes estimator, which are linked to Bayes error rate.
He most often published in these fields:
- Econometrics (32.68%)
- Bayesian probability (29.41%)
- Statistics (26.14%)
What were the highlights of his more recent work (between 2009-2021)?
- Econometrics (32.68%)
- Bayesian probability (29.41%)
- Statistics (26.14%)
In recent papers he was focusing on the following fields of study:
Anthony O'Hagan focuses on Econometrics, Bayesian probability, Statistics, Prior probability and Artificial intelligence.
Anthony O'Hagan has included themes like Location parameter, Actuarial science, Outlier, Statistical model and Normal distribution in his Econometrics study.
Anthony O'Hagan interconnects Environmental ethics, Data mining and Politics in the investigation of issues within Bayesian probability.
In the subject of general Statistics, his work in Sample and Covariate is often linked to Gaussian process, Consistent estimator and Average cost, thereby combining diverse domains of study.
His research in Artificial intelligence intersects with topics in Sensitivity analysis, Machine learning, Expert elicitation and Statistical inference.
Anthony O'Hagan studied Machine learning and Bayesian inference that intersect with Hierarchical database model.
Between 2009 and 2021, his most popular works were:
- Review of statistical methods for analysing healthcare resources and costs. (418 citations)
- Bayesian emulation of complex multi-output and dynamic computer models (307 citations)
- Robust meta‐analytic‐predictive priors in clinical trials with historical control information (138 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Probability distribution
His scientific interests lie mostly in Econometrics, Bayesian probability, Statistical model, Data mining and Bayesian inference.
His study looks at the relationship between Econometrics and topics such as Normal distribution, which overlap with Parametric statistics, Skewness, Markov chain and Linear model.
His Parametric statistics study results in a more complete grasp of Statistics.
His studies in Bayesian probability integrate themes in fields like Conflict resolution, Outlier, Resolution and Location parameter.
As a member of one scientific family, he mostly works in the field of Bayesian inference, focusing on Control engineering and, on occasion, Uncertainty analysis.
In his study, Artificial intelligence and Machine learning is strongly linked to Operations research, which falls under the umbrella field of Uncertainty analysis.
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