David J. Nott

According to our database, David J. Nott authored at least 36 papers between 1997 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

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

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., February, 2023

Correction to : Variational inference and sparsity in high-dimensional deep Gaussian mixture models.

Lucas Kock, Nadja Klein, David J. Nott

Stat. Comput., 2023

Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families.

Benedikt Lütke Schwienhorst, Lucas Kock, David J. Nott, Nadja Klein

CoRR, 2023

Misspecification-robust Sequential Neural Likelihood.

Ryan P. Kelly, David J. Nott, David T. Frazier, David J. Warne, Chris Drovandi

CoRR, 2023

2022

Variational inference and sparsity in high-dimensional deep Gaussian mixture models.

Lucas Kock, Nadja Klein, David J. Nott

Stat. Comput., 2022

2021

Detecting conflicting summary statistics in likelihood-free inference.

Yinan Mao, Xueou Wang, David J. Nott, Michael Evans

Stat. Comput., 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

Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance.

Xuejun Yu, David J. Nott, Minh-Ngoc Tran, Nadja Klein

J. Comput. Graph. Stat., 2021

Marginally Calibrated Deep Distributional Regression.

Nadja Klein, David J. Nott, Michael Stanley Smith

J. Comput. Graph. Stat., 2021

2020

Conditionally structured variational Gaussian approximation with importance weights.

Linda S. L. Tan, Aishwarya Bhaskaran, David J. Nott

Stat. Comput., 2020

Likelihood-free approximate Gibbs sampling.

G. S. Rodrigues, David J. Nott, Scott A. Sisson

Stat. Comput., 2020

Robust Bayesian synthetic likelihood via a semi-parametric approach.

Ziwen An, David J. Nott, Christopher C. Drovandi

Stat. Comput., 2020

2018

Using History Matching for Prior Choice.

Xueou Wang, David J. Nott, Christopher C. Drovandi, Kerrie L. Mengersen, Michael Evans

Technometrics, 2018

Gaussian variational approximation with sparse precision matrices.

Linda S. L. Tan, David J. Nott

Stat. Comput., 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

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

2017

Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model.

Jialiang Li, David J. Nott, Y. Fan, Scott A. Sisson

Comput. Stat. Data Anal., 2017

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

Functional regression approximate Bayesian computation for Gaussian process density estimation.

G. S. Rodrigues, David J. Nott, Scott A. Sisson

Comput. Stat. Data Anal., 2016

2014

Mixtures of experts for understanding model discrepancy in dynamic computer models.

David J. Nott, Lucy Marshall, Mark Fielding, Shie-Yui Liong

Comput. Stat. Data Anal., 2014

2012

The predictive Lasso.

Minh-Ngoc Tran, David J. Nott, Chenlei Leng

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 ensemble Kalman filter is an ABC algorithm.

David J. Nott, Lucy Marshall, Minh-Ngoc Tran

Stat. Comput., 2012

2011

Efficient MCMC Schemes for Computationally Expensive Posterior Distributions.

Mark Fielding, David J. Nott, Shie-Yui Liong

Technometrics, 2011

2010

A sign based loss approach to model selection in nonparametric regression.

David J. Nott, Jialiang Li

Stat. Comput., 2010

Bayesian projection approaches to variable selection in generalized linear models.

David J. Nott, Chenlei Leng

Comput. Stat. Data Anal., 2010

2008

Predictive performance of Dirichlet process shrinkage methods in linear regression.

David J. Nott

Comput. Stat. Data Anal., 2008

2007

A general approach to heteroscedastic linear regression.

David S. Leslie, Robert Kohn, David J. Nott

Stat. Comput., 2007

2006

Semiparametric estimation of mean and variance functions for non-Gaussian data.

David J. Nott

Comput. Stat., 2006

Deflection Routing on a Torus Is Monotone.

Ana Busic, Jean-Michel Fourneau, David J. Nott

Proceedings of the Positive Systems, 2006

2005

Efficient sampling schemes for Bayesian MARS models with many predictors.

David J. Nott, Anthony Y. C. Kuk, Hiep Duc

Stat. Comput., 2005

Convergence Routing under Bursty Traffic: Instability and an AIMD Controller.

Jean-Michel Fourneau, David J. Nott

Proceedings of the Second International Workshop on the Practical Application of Stochastic Modeling, 2005

2004

Mixed Routing for ROMEO Optical Burst.

Jean-Michel Fourneau, David J. Nott

Proceedings of the Computer and Information Sciences, 2004

2000

Multi-phase image modelling with excursion sets.

David J. Nott, Richard J. Wilson

Signal Process., 2000

1997

Parameter estimation for excursion set texture models.

David J. Nott, Richard J. Wilson

Signal Process., 1997