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.
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.
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.
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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Bayesian Calibration of computer models
Marc C. Kennedy;Anthony O'Hagan.
Journal of The Royal Statistical Society Series B-statistical Methodology (2001)
Uncertain Judgements: Eliciting Experts' Probabilities
Anthony O'Hagan;Caitlin E. Buck;Alireza Daneshkhah;J. Richard Eiser.
Predicting the output from a complex computer code when fast approximations are available
MC Kennedy;A O'Hagan.
Probabilistic sensitivity analysis of complex models: a Bayesian approach
Jeremy E. Oakley;Anthony O'Hagan.
Journal of The Royal Statistical Society Series B-statistical Methodology (2004)
Fractional Bayes factors for model comparison
Journal of the royal statistical society series b-methodological (1995)
Statistical Methods for Eliciting Probability Distributions
Paul H Garthwaite;Joseph B Kadane;Anthony O'Hagan.
Journal of the American Statistical Association (2005)
Curve Fitting and Optimal Design for Prediction
Journal of the royal statistical society series b-methodological (1978)
Bayesian analysis of computer code outputs: A tutorial
Reliability Engineering & System Safety (2006)
An overview of robust Bayesian analysis
James O. Berger;Elías Moreno;Luis Raul Pericchi;M. Jesús Bayarri.
Review of statistical methods for analysing healthcare resources and costs.
Borislava Mihaylova;Andrew Briggs;Anthony O'Hagan;Simon G. Thompson.
Health Economics (2011)
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