World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
51
Citations
18720
World Ranking
994
National Ranking
76

Overview

A. P. Dawid is affiliated with the University of Cambridge in the United Kingdom and conducts research primarily in the fields of Mathematics and Computer Science. The scientist's work focuses heavily on Statistics and Probability as well as Artificial Intelligence, with additional involvement in Economics and Econometrics, Infectious Diseases, and Management Science and Operations Research.

Their research topics encompass a range of statistical and causal inference methodologies. Key topics include:

  • Advanced Causal Inference Techniques
  • Bayesian Modeling and Causal Inference
  • Statistical Methods and Bayesian Inference
  • Statistical Methods and Inference
  • COVID-19 epidemiological studies
  • Viral Infections and Outbreaks Research
  • Qualitative Comparative Analysis Research

A. P. Dawid's recent publications address various foundational and applied aspects of statistical causality and pandemic modeling. Notable papers include:

  • "Key questions for modelling COVID-19 exit strategies," 2020, Proceedings of the Royal Society B Biological Sciences
  • "Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling," 2022, Epidemics
  • "Decision-theoretic foundations for statistical causality," 2021, Journal of Causal Inference
  • "Decision-theoretic foundations for statistical causality," 2020, arXiv (Cornell University)
  • "Bounding Causes of Effects With Mediators," 2024, UNICA IRIS Institutional Research Information System (University of Cagliari)

The scientist frequently publishes in several academic venues, including:

  • arXiv (Cornell University)
  • Journal of Causal Inference
  • Proceedings of the Royal Society B Biological Sciences
  • International Journal of Approximate Reasoning
  • Journal of the Royal Statistical Society Series A (Statistics in Society)

A. P. Dawid has collaborated repeatedly with a number of researchers, notably:

  • Monica Musio
  • Peter Challenor
  • Trevelyan J. McKinley
  • Lorenzo Pellis
  • Julia Mortera

Best Publications

  • Conditional Independence in Statistical Theory

    A. P. Dawid

  • Maximum Likelihood Estimation of Observer Error-Rates Using the EM Algorithm

    A. P. Dawid;A. M. Skene

  • Independence properties of directed markov fields

    S. L. Lauritzen;A. P. Dawid;B. N. Larsen;H.-G. Leimer

  • The Well-Calibrated Bayesian

    A. P. Dawid

  • Hyper Markov Laws in the Statistical Analysis of Decomposable Graphical Models

    A.P. Dawid;Steffen Lilholt Lauritzen

  • Causal Inference without Counterfactuals

    A. P. Dawid

  • Some matrix-variate distribution theory: Notational considerations and a Bayesian application

    A. P. Dawid

  • On Bayesian analysis of mixtures with an unknown number of components. Discussion. Author's reply

    S. Richardson;P. J. Green;C. P. Robert;M. Aitkin

  • Marginalization Paradoxes in Bayesian and Structural Inference

    A. P. Dawid;M. Stone;J. V. Zidek

  • Applications of a general propagation algorithm for probabilistic expert systems

    A. P. Dawid

  • Influence Diagrams for Causal Modelling and Inference

    A. P. Dawid

  • Generative or Discriminative? Getting the Best of Both Worlds

    Julia Lasserre;Christopher M. Bishop;J. M. Bernardo;M. J. Bayarri

  • Bayesian Statistics 5.

    T. Leonard;J. M. Bernado;J. O. Berger;A. P. Dawid

  • Properties of diagnostic data distributions.

    Dawid Ap

  • Calibration-Based Empirical Probability

    A. P. Dawid

  • The Functional-Model Basis of Fiducial Inference

    A. P. Dawid;M. Stone

  • Probabilistic Expert Systems for Forensic Inference from Genetic Markers

    A. P. Dawid;J. Mortera;Vincenzo Lorenzo Pascali;D. Van Boxel

  • Posterior expectations for large observations

    A. P. Dawid

  • Probabilistic expert systems for DNA mixture profiling.

    J. Mortera;A. P. Dawid;Steffen Lilholt Lauritzen

  • Encyclopedia of Statistical Sciences 2.

    A. P. Dawid;S. Kotz;N. L. Johnson;C. B. Read

  • STOCHASTIC COMPLEXITY IN STATISTICAL INQUIRY

    Unknown

Frequent Co-Authors

Steffen L. Lauritzen
Steffen L. Lauritzen University of Copenhagen
James O. Berger
James O. Berger Duke University
David R. Cox
David R. Cox University of Oxford
Adrian F. M. Smith
Adrian F. M. Smith Imperial College London
David Heckerman
David Heckerman Microsoft (United States)
Christopher Jennison
Christopher Jennison University of Bath
Samuel Kotz
Samuel Kotz George Washington University
Joseph B. Kadane
Joseph B. Kadane Carnegie Mellon University
Christopher M. Bishop
Christopher M. Bishop Microsoft (United States)
Larry Wasserman
Larry Wasserman Carnegie Mellon University

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