David Nott
David Nott
Associate Professor of Statistics, National University of Singapore
Verified email at nus.edu.sg - Homepage
Cited by
Cited by
A comparative study of Markov chain Monte Carlo methods for conceptual rainfall‐runoff modeling
L Marshall, D Nott, A Sharma
Water Resources Research 40 (2), 2004
Adaptive sampling for Bayesian variable selection
DJ Nott, R Kohn
Biometrika 92 (4), 747-763, 2005
Bayesian synthetic likelihood
LF Price, CC Drovandi, A Lee, DJ Nott
Journal of Computational and Graphical Statistics 27 (1), 1-11, 2018
Bayesian adaptive lasso
C Leng, MN Tran, D Nott
Annals of the Institute of Statistical Mathematics 66 (2), 221-244, 2014
Towards dynamic catchment modelling: a Bayesian hierarchical mixtures of experts framework
L Marshall, D Nott, A Sharma
Hydrological Processes: An International Journal 21 (7), 847-861, 2007
Meta-analysis and gene set enrichment relative to er status reveal elevated activity of MYC and E2F in the “basal” breast cancer subgroup
MC Alles, M Gardiner-Garden, DJ Nott, Y Wang, JA Foekens, ...
PloS one 4 (3), e4710, 2009
Hydrological model selection: A Bayesian alternative
L Marshall, D Nott, A Sharma
Water resources research 41 (10), 2005
A pairwise likelihood approach to analyzing correlated binary data
AYC Kuk, DJ Nott
Statistics & Probability Letters 47 (4), 329-335, 2000
Pairwise likelihood methods for inference in image models
DJ Nott, T Rydén
Biometrika 86 (3), 661-676, 1999
Bayesian variable selection and the Swendsen-Wang algorithm
DJ Nott, PJ Green
Journal of computational and Graphical Statistics 13 (1), 141-157, 2004
Estimation of nonstationary spatial covariance structure
DJ Nott, WTM Dunsmuir
Biometrika 89 (4), 819-829, 2002
Variational Bayes with intractable likelihood
MN Tran, DJ Nott, R Kohn
Journal of Computational and Graphical Statistics 26 (4), 873-882, 2017
Modeling the catchment via mixtures: Issues of model specification and validation
L Marshall, A Sharma, D Nott
Water resources research 42 (11), 2006
Generalized likelihood uncertainty estimation (GLUE) and approximate Bayesian computation: What's the connection?
DJ Nott, L Marshall, J Brown
Water Resources Research 48 (12), 2012
Variational Bayes with synthetic likelihood
VMH Ong, DJ Nott, MN Tran, SA Sisson, CC Drovandi
Statistics and Computing 28 (4), 971-988, 2018
Approximate Bayesian computation via regression density estimation
Y Fan, DJ Nott, SA Sisson
Stat 2 (1), 34-48, 2013
Sampling schemes for Bayesian variable selection in generalized linear models
DJ Nott, D Leonte
Journal of Computational and Graphical Statistics 13 (2), 362-382, 2004
Approximate Bayesian computation and Bayes’ linear analysis: Toward high-dimensional ABC
DJ Nott, Y Fan, L Marshall, SA Sisson
Journal of Computational and Graphical Statistics 23 (1), 65-86, 2014
Monte Carlo sampling from the quantum state space. I
J Shang, YL Seah, HK Ng, DJ Nott, BG Englert
New Journal of Physics 17 (4), 043017, 2015
Efficient MCMC schemes for computationally expensive posterior distributions
M Fielding, DJ Nott, SY Liong
Technometrics 53 (1), 16-28, 2011
The system can't perform the operation now. Try again later.
Articles 1–20