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David J. Nott
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2020 – today
- 2024
- [j33]Christopher C. Drovandi
, David J. Nott, David T. Frazier:
Improving the Accuracy of Marginal Approximations in Likelihood-Free Inference via Localization. J. Comput. Graph. Stat. 33(1): 101-111 (2024) - [j32]David Gunawan, Robert Kohn
Flexible Variational Bayes Based on a Copula of a Mixture. J. Comput. Graph. Stat. 33(2): 665-680 (2024) - [j31]Ryan P. Kelly, David J. Nott, David T. Frazier, David J. Warne, Christopher C. Drovandi:
Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference. Trans. Mach. Learn. Res. 2024 (2024) - [c4]Benedikt Lütke Schwienhorst, Lucas Kock, Nadja Klein, David J. Nott:
Dropout Regularization in Extended Generalized Linear Models Based on Double Exponential Families. ECML/PKDD (6) 2024: 320-336 - [i4]R. Torres, David J. Nott, Scott A. Sisson, T. Rodrigues, J. G. Reis, G. S. Rodrigues:
Model-Free Local Recalibration of Neural Networks. CoRR abs/2403.05756 (2024) - [i3]William E. R. de Amorim, Scott A. Sisson, T. Rodrigues, David J. Nott, Guilherme S. Rodrigues:
Positional Encoder Graph Quantile Neural Networks for Geographic Data. CoRR abs/2409.18865 (2024) - 2023
- [j30]Lucas Kock, Nadja Klein, David J. Nott:
Correction to : Variational inference and sparsity in high-dimensional deep Gaussian mixture models. Stat. Comput. 33(1): 24 (2023) - [j29]Atlanta Chakraborty, David J. Nott, Christopher C. Drovandi, David T. Frazier, Scott A. Sisson:
Modularized Bayesian analyses and cutting feedback in likelihood-free inference. Stat. Comput. 33(1): 33 (2023) - [i2]Ryan P. Kelly
Misspecification-robust Sequential Neural Likelihood. CoRR abs/2301.13368 (2023) - [i1]Benedikt Lütke Schwienhorst, Lucas Kock
Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families. CoRR abs/2305.06625 (2023) - 2022
- [j28]Lucas Kock
Variational inference and sparsity in high-dimensional deep Gaussian mixture models. Stat. Comput. 32(5): 70 (2022) - 2021
- [j27]Nadja Klein
Marginally Calibrated Deep Distributional Regression. J. Comput. Graph. Stat. 30(2): 467-483 (2021) - [j26]Xuejun Yu, David J. Nott
Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance. J. Comput. Graph. Stat. 30(4): 977-990 (2021) - [j25]Christopher C. Drovandi
A Semiautomatic Method for History Matching Using Sequential Monte Carlo. SIAM/ASA J. Uncertain. Quantification 9(3): 1034-1063 (2021) - [j24]Yinan Mao, Xueou Wang, David J. Nott
Detecting conflicting summary statistics in likelihood-free inference. Stat. Comput. 31(6): 78 (2021) - 2020
- [j23]Ziwen An
Robust Bayesian synthetic likelihood via a semi-parametric approach. Stat. Comput. 30(3): 543-557 (2020) - [j22]G. S. Rodrigues, David J. Nott
Likelihood-free approximate Gibbs sampling. Stat. Comput. 30(4): 1057-1073 (2020) - [j21]Linda S. L. Tan
Conditionally structured variational Gaussian approximation with importance weights. Stat. Comput. 30(5): 1255-1272 (2020)
2010 – 2019
- 2018
- [j20]Victor M. H. Ong, David J. Nott
Likelihood-free inference in high dimensions with synthetic likelihood. Comput. Stat. Data Anal. 128: 271-291 (2018) - [j19]Linda S. L. Tan
Gaussian variational approximation with sparse precision matrices. Stat. Comput. 28(2): 259-275 (2018) - [j18]Victor M. H. Ong
Variational Bayes with synthetic likelihood. Stat. Comput. 28(4): 971-988 (2018) - [j17]Xueou Wang, David J. Nott
Using History Matching for Prior Choice. Technometrics 60(4): 445-460 (2018) - 2017
- [j16]Jialiang Li
Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model. Comput. Stat. Data Anal. 106: 77-89 (2017) - 2016
- [j15]G. S. Rodrigues, David J. Nott
Functional regression approximate Bayesian computation for Gaussian process density estimation. Comput. Stat. Data Anal. 103: 229-241 (2016) - [j14]Linda S. L. Tan
Variational inference for sparse spectrum Gaussian process regression. Stat. Comput. 26(6): 1243-1261 (2016) - 2014
- [j13]David J. Nott
Mixtures of experts for understanding model discrepancy in dynamic computer models. Comput. Stat. Data Anal. 71: 491-505 (2014) - 2012
- [j12]David J. Nott
Variational approximation for heteroscedastic linear models and matching pursuit algorithms. Stat. Comput. 22(2): 497-512 (2012) - [j11]Minh-Ngoc Tran, David J. Nott
The predictive Lasso. Stat. Comput. 22(5): 1069-1084 (2012) - [j10]David J. Nott
The ensemble Kalman filter is an ABC algorithm. Stat. Comput. 22(6): 1273-1276 (2012) - 2011
- [j9]Mark Fielding, David J. Nott
Efficient MCMC Schemes for Computationally Expensive Posterior Distributions. Technometrics 53(1): 16-28 (2011) - 2010
- [j8]David J. Nott
Bayesian projection approaches to variable selection in generalized linear models. Comput. Stat. Data Anal. 54(12): 3227-3241 (2010) - [j7]David J. Nott
A sign based loss approach to model selection in nonparametric regression. Stat. Comput. 20(4): 485-498 (2010)
2000 – 2009
- 2008
- [j6]David J. Nott
Predictive performance of Dirichlet process shrinkage methods in linear regression. Comput. Stat. Data Anal. 52(7): 3658-3669 (2008) - 2007
- [j5]David S. Leslie
A general approach to heteroscedastic linear regression. Stat. Comput. 17(2): 131-146 (2007) - 2006
- [j4]David J. Nott
Semiparametric estimation of mean and variance functions for non-Gaussian data. Comput. Stat. 21(3-4): 603-620 (2006) - [c3]Ana Busic
Deflection Routing on a Torus Is Monotone. POSTA 2006: 161-168 - 2005
- [j3]David J. Nott
Efficient sampling schemes for Bayesian MARS models with many predictors. Stat. Comput. 15(2): 93-101 (2005) - [c2]Jean-Michel Fourneau, David J. Nott:
Convergence Routing under Bursty Traffic: Instability and an AIMD Controller. PASM@FM 2005: 97-109 - 2004
- [c1]Jean-Michel Fourneau, David J. Nott:
Mixed Routing for ROMEO Optical Burst. ISCIS 2004: 257-266 - 2000
- [j2]David J. Nott
Multi-phase image modelling with excursion sets. Signal Process. 80(1): 125-139 (2000)
1990 – 1999
- 1997
- [j1]David J. Nott
Parameter estimation for excursion set texture models. Signal Process. 63(3): 199-210 (1997)
Coauthor Index
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