David J. Nott

Publication Activity (10 Years)

Years Active: 1997-2024
Publications (10 Years): 23

Top Topics

High Dimensions

Variational Inference

Gaussian Mixture

Top Venues

J. Comput. Graph. Stat.

Comput. Stat. Data Anal.

Publications

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)
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) (2024)
R. Torres, David J. Nott, Scott A. Sisson, T. Rodrigues, J. G. Reis, G. S. Rodrigues
Model-Free Local Recalibration of Neural Networks. CoRR (2024)
David Gunawan, Robert Kohn, David J. Nott
Flexible Variational Bayes Based on a Copula of a Mixture. J. Comput. Graph. Stat. 33 (2) (2024)
Ryan P. Kelly, David J. Nott, David T. Frazier, David J. Warne, Chris Drovandi
Misspecification-robust Sequential Neural Likelihood. CoRR (2023)
Benedikt Lütke Schwienhorst, Lucas Kock, David J. Nott, Nadja Klein
Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families. CoRR (2023)
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) (2023)
Lucas Kock, Nadja Klein, David J. Nott
Correction to : Variational inference and sparsity in high-dimensional deep Gaussian mixture models. Stat. Comput. 33 (1) (2023)
Lucas Kock, Nadja Klein, David J. Nott
Variational inference and sparsity in high-dimensional deep Gaussian mixture models. Stat. Comput. 32 (5) (2022)
Christopher C. Drovandi, David J. Nott, Daniel Edward Pagendam
A Semiautomatic Method for History Matching Using Sequential Monte Carlo. SIAM/ASA J. Uncertain. Quantification 9 (3) (2021)
Xuejun Yu, David J. Nott, Minh-Ngoc Tran, Nadja Klein
Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance. J. Comput. Graph. Stat. 30 (4) (2021)
Nadja Klein, David J. Nott, Michael Stanley Smith
Marginally Calibrated Deep Distributional Regression. J. Comput. Graph. Stat. 30 (2) (2021)
Yinan Mao, Xueou Wang, David J. Nott, Michael Evans
Detecting conflicting summary statistics in likelihood-free inference. Stat. Comput. 31 (6) (2021)
Ziwen An, David J. Nott, Christopher C. Drovandi
Robust Bayesian synthetic likelihood via a semi-parametric approach. Stat. Comput. 30 (3) (2020)
G. S. Rodrigues, David J. Nott, Scott A. Sisson
Likelihood-free approximate Gibbs sampling. Stat. Comput. 30 (4) (2020)
Linda S. L. Tan, Aishwarya Bhaskaran, David J. Nott
Conditionally structured variational Gaussian approximation with importance weights. Stat. Comput. 30 (5) (2020)
Victor M. H. Ong, David J. Nott, Minh-Ngoc Tran, Scott A. Sisson, Christopher C. Drovandi
Variational Bayes with synthetic likelihood. Stat. Comput. 28 (4) (2018)
Linda S. L. Tan, David J. Nott
Gaussian variational approximation with sparse precision matrices. Stat. Comput. 28 (2) (2018)
Victor M. H. Ong, David J. Nott, Minh-Ngoc Tran, Scott A. Sisson, Christopher C. Drovandi
Likelihood-free inference in high dimensions with synthetic likelihood. Comput. Stat. Data Anal. 128 (2018)
Xueou Wang, David J. Nott, Christopher C. Drovandi, Kerrie L. Mengersen, Michael Evans
Using History Matching for Prior Choice. Technometrics 60 (4) (2018)
Jialiang Li, David J. Nott, Y. Fan, Scott A. Sisson
Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model. Comput. Stat. Data Anal. 106 (2017)
Linda S. L. Tan, Victor M. H. Ong, David J. Nott, Ajay Jasra
Variational inference for sparse spectrum Gaussian process regression. Stat. Comput. 26 (6) (2016)
G. S. Rodrigues, David J. Nott, Scott A. Sisson
Functional regression approximate Bayesian computation for Gaussian process density estimation. Comput. Stat. Data Anal. 103 (2016)
David J. Nott, Lucy Marshall, Mark Fielding, Shie-Yui Liong
Mixtures of experts for understanding model discrepancy in dynamic computer models. Comput. Stat. Data Anal. 71 (2014)
David J. Nott, Lucy Marshall, Minh-Ngoc Tran
The ensemble Kalman filter is an ABC algorithm. Stat. Comput. 22 (6) (2012)
David J. Nott, Minh-Ngoc Tran, Chenlei Leng
Variational approximation for heteroscedastic linear models and matching pursuit algorithms. Stat. Comput. 22 (2) (2012)
Minh-Ngoc Tran, David J. Nott, Chenlei Leng
The predictive Lasso. Stat. Comput. 22 (5) (2012)
Mark Fielding, David J. Nott, Shie-Yui Liong
Efficient MCMC Schemes for Computationally Expensive Posterior Distributions. Technometrics 53 (1) (2011)
David J. Nott, Chenlei Leng
Bayesian projection approaches to variable selection in generalized linear models. Comput. Stat. Data Anal. 54 (12) (2010)
David J. Nott, Jialiang Li
A sign based loss approach to model selection in nonparametric regression. Stat. Comput. 20 (4) (2010)
David J. Nott
Predictive performance of Dirichlet process shrinkage methods in linear regression. Comput. Stat. Data Anal. 52 (7) (2008)
David S. Leslie, Robert Kohn, David J. Nott
A general approach to heteroscedastic linear regression. Stat. Comput. 17 (2) (2007)
Ana Busic, Jean-Michel Fourneau, David J. Nott
Deflection Routing on a Torus Is Monotone. POSTA (2006)
David J. Nott, Anthony Y. C. Kuk, Hiep Duc
Efficient sampling schemes for Bayesian MARS models with many predictors. Stat. Comput. 15 (2) (2005)
David J. Nott, Richard J. Wilson
Multi-phase image modelling with excursion sets. Signal Process. 80 (1) (2000)
David J. Nott, Richard J. Wilson
Parameter estimation for excursion set texture models. Signal Process. 63 (3) (1997)