World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
32
Citations
3581
World Ranking
3238
National Ranking
9

Computer Science

D-Index
31
Citations
3563
World Ranking
13735
National Ranking
184

David J. Nott publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where David J. Nott sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 188 publications — 57th percentile

57% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

David J. Nott D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where David J. Nott sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 32 D-Index — 14th percentile

14% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

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
Raul Tempone
Raul Tempone King Abdullah University of Science and Technology
Peter J. Danaher
Peter J. Danaher Monash University
Christine A. Shoemaker
Christine A. Shoemaker National University of Singapore
Christopher K. Wikle
Christopher K. Wikle University of Missouri
Jennifer Seberry
Jennifer Seberry University of Wollongong

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