Parameter estimation for excursion set texture models. - David J. Nott; Richard J. Wilson 001 (Signal Process., 1997)

Multi-phase image modelling with excursion sets. - David J. Nott; Richard J. Wilson 001 (Signal Process., 2000)

Mixed Routing for ROMEO Optical Burst. - Jean-Michel Fourneau; David J. Nott (ISCIS, 2004)

Convergence Routing under Bursty Traffic: Instability and an AIMD Controller. - Jean-Michel Fourneau; David J. Nott (PASM@FM, 2005)

Efficient sampling schemes for Bayesian MARS models with many predictors. - David J. Nott; Anthony Y. C. Kuk; Hiep Duc (Stat. Comput., 2005)

Deflection Routing on a Torus Is Monotone. - Ana Busic; Jean-Michel Fourneau; David J. Nott (POSTA, 2006)

Semiparametric estimation of mean and variance functions for non-Gaussian data. - David J. Nott (Comput. Stat., 2006)

A general approach to heteroscedastic linear regression. - David S. Leslie; Robert Kohn; David J. Nott (Stat. Comput., 2007)

Predictive performance of Dirichlet process shrinkage methods in linear regression. - David J. Nott (Comput. Stat. Data Anal., 2008)

Bayesian projection approaches to variable selection in generalized linear models. - David J. Nott; Chenlei Leng (Comput. Stat. Data Anal., 2010)

A sign based loss approach to model selection in nonparametric regression. - David J. Nott; Jialiang Li 001 (Stat. Comput., 2010)

Efficient MCMC Schemes for Computationally Expensive Posterior Distributions. - Mark Fielding; David J. Nott; Shie-Yui Liong (Technometrics, 2011)

The ensemble Kalman filter is an ABC algorithm. - David J. Nott; Lucy A. Marshall; Minh-Ngoc Tran (Stat. Comput., 2012)

Variational approximation for heteroscedastic linear models and matching pursuit algorithms. - David J. Nott; Minh-Ngoc Tran; Chenlei Leng (Stat. Comput., 2012)

The predictive Lasso. - Minh-Ngoc Tran; David J. Nott; Chenlei Leng (Stat. Comput., 2012)

Mixtures of experts for understanding model discrepancy in dynamic computer models. - David J. Nott; Lucy A. Marshall; Mark Fielding; Shie-Yui Liong (Comput. Stat. Data Anal., 2014)

Functional regression approximate Bayesian computation for Gaussian process density estimation. - G. S. Rodrigues; David J. Nott; Scott A. Sisson (Comput. Stat. Data Anal., 2016)

Variational inference for sparse spectrum Gaussian process regression. - Linda S. L. Tan; Victor M. H. Ong; David J. Nott; Ajay Jasra (Stat. Comput., 2016)

Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model. - Jialiang Li 001; David J. Nott; Y. Fan; Scott A. Sisson (Comput. Stat. Data Anal., 2017)

Likelihood-free inference in high dimensions with synthetic likelihood. - Victor M. H. Ong; David J. Nott; Minh-Ngoc Tran; Scott A. Sisson; Christopher C. Drovandi (Comput. Stat. Data Anal., 2018)

Variational Bayes with synthetic likelihood. - Victor M. H. Ong; David J. Nott; Minh-Ngoc Tran; Scott A. Sisson; Christopher C. Drovandi (Stat. Comput., 2018)

Gaussian variational approximation with sparse precision matrices. - Linda S. L. Tan; David J. Nott (Stat. Comput., 2018)

Using History Matching for Prior Choice. - Xueou Wang; David J. Nott; Christopher C. Drovandi; Kerrie L. Mengersen; Michael Evans (Technometrics, 2018)

Robust Bayesian synthetic likelihood via a semi-parametric approach. - Ziwen An; David J. Nott; Christopher C. Drovandi (Stat. Comput., 2020)

Likelihood-free approximate Gibbs sampling. - G. S. Rodrigues; David J. Nott; Scott A. Sisson (Stat. Comput., 2020)

Conditionally structured variational Gaussian approximation with importance weights. - Linda S. L. Tan; Aishwarya Bhaskaran; David J. Nott (Stat. Comput., 2020)

Marginally Calibrated Deep Distributional Regression. - Nadja Klein; David J. Nott; Michael Stanley Smith (J. Comput. Graph. Stat., 2021)

Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance. - Xuejun Yu 002; David J. Nott; Minh-Ngoc Tran; Nadja Klein (J. Comput. Graph. Stat., 2021)

A Semiautomatic Method for History Matching Using Sequential Monte Carlo. - Christopher C. Drovandi; David J. Nott; Daniel Edward Pagendam (SIAM/ASA J. Uncertain. Quantification, 2021)

Detecting conflicting summary statistics in likelihood-free inference. - Yinan Mao; Xueou Wang; David J. Nott; Michael Evans (Stat. Comput., 2021)

Variational inference and sparsity in high-dimensional deep Gaussian mixture models. - Lucas Kock; Nadja Klein; David J. Nott (Stat. Comput., 2022)

Misspecification-robust Sequential Neural Likelihood. - Ryan P. Kelly; David J. Nott; David T. Frazier; David J. Warne; Chris Drovandi (CoRR, 2023)

Correction to : Variational inference and sparsity in high-dimensional deep Gaussian mixture models. - Lucas Kock; Nadja Klein; David J. Nott (Stat. Comput., 2023)

Modularized Bayesian analyses and cutting feedback in likelihood-free inference. - Atlanta Chakraborty; David J. Nott; Christopher C. Drovandi; David T. Frazier; Scott A. Sisson (Stat. Comput., 2023)

Dropout Regularization in Extended Generalized Linear Models Based on Double Exponential Families. - Benedikt Lütke Schwienhorst; Lucas Kock; Nadja Klein; David J. Nott (ECML/PKDD (6), 2024)

Model-Free Local Recalibration of Neural Networks. - R. Torres; David J. Nott; Scott A. Sisson; T. Rodrigues; J. G. Reis; G. S. Rodrigues (CoRR, 2024)

Positional Encoder Graph Quantile Neural Networks for Geographic Data. - William E. R. de Amorim; Scott A. Sisson; T. Rodrigues; David J. Nott; Guilherme S. Rodrigues (CoRR, 2024)

Improving the Accuracy of Marginal Approximations in Likelihood-Free Inference via Localization. - Christopher C. Drovandi; David J. Nott; David T. Frazier (J. Comput. Graph. Stat., 2024)

Flexible Variational Bayes Based on a Copula of a Mixture. - David Gunawan; Robert Kohn; David J. Nott (J. Comput. Graph. Stat., 2024)

Structured Variational Approximations with Skew Normal Decomposable Graphical Models and Implicit Copulas. - Robert Salomone; Xuejun Yu 002; David J. Nott; Robert Kohn (J. Comput. Graph. Stat., 2024)

Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference. - Ryan P. Kelly; David J. Nott; David T. Frazier; David J. Warne; Christopher C. Drovandi (Trans. Mach. Learn. Res., 2024)

Multi-objective Bayesian optimization for Likelihood-Free inference in sequential sampling models of decision making. - David Chen 007; Xinwei Li; Eui-Jin Kim; Prateek Bansal; David J. Nott (Trans. Mach. Learn. Res., 2025)

Simulation-based Bayesian inference under model misspecification. - Ryan P. Kelly; David J. Warne; David T. Frazier; David J. Nott; Michael U. Gutmann; Christopher C. Drovandi (CoRR, 2025)