| 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 | 258 | 2004 |
| Adaptive sampling for Bayesian variable selection DJ Nott, R Kohn Biometrika 92 (4), 747-763, 2005 | 125 | 2005 |
| Bayesian synthetic likelihood LF Price, CC Drovandi, A Lee, DJ Nott Journal of Computational and Graphical Statistics 27 (1), 1-11, 2018 | 120 | 2018 |
| Bayesian adaptive lasso C Leng, MN Tran, D Nott Annals of the Institute of Statistical Mathematics 66 (2), 221-244, 2014 | 112 | 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 | 100 | 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 | 97 | 2009 |
| A pairwise likelihood approach to analyzing correlated binary data AYC Kuk, DJ Nott Statistics & Probability Letters 47 (4), 329-335, 2000 | 92 | 2000 |
| Hydrological model selection: A Bayesian alternative L Marshall, D Nott, A Sharma Water resources research 41 (10), 2005 | 91 | 2005 |
| Pairwise likelihood methods for inference in image models DJ Nott, T Rydén Biometrika 86 (3), 661-676, 1999 | 85 | 1999 |
| Bayesian variable selection and the Swendsen-Wang algorithm DJ Nott, PJ Green Journal of computational and Graphical Statistics 13 (1), 141-157, 2004 | 80 | 2004 |
| Estimation of nonstationary spatial covariance structure DJ Nott, WTM Dunsmuir Biometrika 89 (4), 819-829, 2002 | 79 | 2002 |
| Variational Bayes with intractable likelihood MN Tran, DJ Nott, R Kohn Journal of Computational and Graphical Statistics 26 (4), 873-882, 2017 | 75 | 2017 |
| Modeling the catchment via mixtures: Issues of model specification and validation L Marshall, A Sharma, D Nott Water resources research 42 (11), 2006 | 64 | 2006 |
| Approximate Bayesian computation via regression density estimation Y Fan, DJ Nott, SA Sisson Stat 2 (1), 34-48, 2013 | 54 | 2013 |
| 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 | 54 | 2012 |
| Variational Bayes with synthetic likelihood VMH Ong, DJ Nott, MN Tran, SA Sisson, CC Drovandi Statistics and Computing 28 (4), 971-988, 2018 | 53 | 2018 |
| 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 | 50 | 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 | 48 | 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 | 47* | 2015 |
| Efficient MCMC schemes for computationally expensive posterior distributions M Fielding, DJ Nott, SY Liong Technometrics 53 (1), 16-28, 2011 | 45 | 2011 |
