World's Best Scientists 2026 revealed!

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Computer Science

D-Index
31
Citations
3563
World Ranking
13738
National Ranking
184

Mathematics

D-Index
32
Citations
3581
World Ranking
3237
National Ranking
9

Overview

David J. Nott is affiliated with the National University of Singapore in Singapore. Their research spans multiple fields, primarily focusing on Computer Science and Mathematics. Within these broad areas, they have contributed extensively to subfields such as Statistics and Probability, Artificial Intelligence, Finance, Statistics, Probability and Uncertainty, as well as Management Science and Operations Research.

Their main research topics include Statistical Methods and Bayesian Inference, Gaussian Processes and Bayesian Inference, Bayesian Methods and Mixture Models, Statistical Methods and Inference, Markov Chains and Monte Carlo Methods, Bayesian Modeling and Causal Inference, and Advanced Statistical Methods and Models.

David J. Nott has authored papers in a variety of venues, with notable frequency in:

  • arXiv (Cornell University)
  • Journal of Computational and Graphical Statistics
  • Statistics and Computing
  • Journal of the American Statistical Association
  • Bayesian Analysis

Some of the recent papers include:

  • "Reversal and Remission of T2DM - An Update for Practitioners" (2022) published in Vascular Health and Risk Management
  • "Bayesian Inference Using Synthetic Likelihood: Asymptotics and Adjustments" (2022) published in Journal of the American Statistical Association
  • "High-Dimensional Copula Variational Approximation Through Transformation" (2020) published in Journal of Computational and Graphical Statistics
  • "Likelihood-free approximate Gibbs sampling" (2020) published in Statistics and Computing
  • "Variational Bayes approximation of factor stochastic volatility models" (2021) published in International Journal of Forecasting

In the scope of book publications, David J. Nott has contributed to a title published by Elsevier BV:

  • Flexible Bayesian Regression Modelling (2020)

Collaborations are an important part of their work. Frequent co-authors include:

  • David T. Frazier
  • Christopher Drovandi
  • Nadja Klein
  • Michael S. Smith
  • Robert Kohn

Best Publications

  • A comparative study of Markov chain Monte Carlo methods for conceptual rainfall‐runoff modeling

    Lucy Marshall;David Nott;Ashish Sharma

  • Bayesian Synthetic Likelihood

    Leah F. Price;Christopher C. Drovandi;Anthony Lee;David J. Nott

  • Bayesian adaptive Lasso

    Chenlei Leng;Chenlei Leng;Minh-Ngoc Tran;David Nott

  • Adaptive sampling for Bayesian variable selection

    David J. Nott;Robert Kohn

  • Adaptive sampling for Bayesian variable selection

    David J Nott;Robert Kohn

  • Hydrological model selection: A Bayesian alternative

    Lucy Marshall;David Nott;Ashish Sharma

  • Meta-analysis and gene set enrichment relative to er status reveal elevated activity of MYC and E2F in the "basal" breast cancer subgroup.

    M. Chehani Alles;Margaret Gardiner-Garden;David J. Nott;Yixin Wang

  • Towards dynamic catchment modelling: a Bayesian hierarchical mixtures of experts framework

    Lucy Marshall;David Nott;Ashish Sharma

  • Variational Bayes With Intractable Likelihood

    Minh-Ngoc Tran;David J. Nott;Robert Kohn

  • A pairwise likelihood approach to analyzing correlated binary data

    Anthony Y.C. Kuk;David J. Nott

  • Generalized likelihood uncertainty estimation (GLUE) and approximate Bayesian computation: What's the connection?

    David J. Nott;Lucy Marshall;Jason Brown

  • Estimation of nonstationary spatial covariance structure

    David J. Nott;William T. M. Dunsmuir

  • Pairwise likelihood methods for inference in image models

    David J. Nott;Tobias Ryden

  • Modeling the catchment via mixtures: Issues of model specification and validation

    Lucy Marshall;Ashish Sharma;David Nott

  • Bayesian Variable Selection and the Swendsen-Wang Algorithm

    David J Nott;Peter J Green

  • Gaussian variational approximation with a factor covariance structure

    Victor M.-H. Ong;David J. Nott;Michael S. Smith

  • Bayesian Deep Net GLM and GLMM

    M.-N. Tran;N. Nguyen;D. Nott;R. Kohn

  • Approximate Bayesian computation via regression density estimation

    Yanan Fan;David J. Nott;Scott A. Sisson

  • Approximate Bayesian Computation and Bayes’ Linear Analysis: Toward High-Dimensional ABC

    D. J. Nott;Y. Fan;L. Marshall;S. A. Sisson

  • Variational Bayes with synthetic likelihood

    Victor M. H. Ong;David J. Nott;Minh-Ngoc Tran;Scott A. Sisson

  • Efficient MCMC Schemes for Computationally Expensive Posterior Distributions

    Mark Fielding;David J. Nott;Shie-Yui Liong

  • Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model

    J. Li;D.J. Nott;Y. Fan;S.A. Sisson

  • Gaussian variational approximation with sparse precision matrices

    Linda S. L. Tan;David J. Nott

  • Variational Bayes with Synthetic Likelihood

    Victor M-H. Ong;David J. Nott;Minh-Ngoc Tran;Scott A. Sisson

Frequent Co-Authors

Scott A. Sisson
Scott A. Sisson University of New South Wales
Ashish Sharma
Ashish Sharma University of New South Wales
Mark J. Cowley
Mark J. Cowley Garvan Institute of Medical Research
Chris Cotsapas
Chris Cotsapas Yale University
Kerrie Mengersen
Kerrie Mengersen Queensland University of Technology
Christine A. Shoemaker
Christine A. Shoemaker National University of Singapore
Raul Tempone
Raul Tempone King Abdullah University of Science and Technology
Christopher K. Wikle
Christopher K. Wikle University of Missouri
Elizabeth A. Musgrove
Elizabeth A. Musgrove University of Glasgow
Jennifer Seberry
Jennifer Seberry University of Wollongong

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