David J. Nott

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Hydrologic Model Selection using Markov chain Monte Carlo methods

Ashish Sharma, Lucy Marshall, David J. Nott, 2002 .

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

David J. Nott, D. Nott, 2006, Comput. Stat..

Efficient sampling schemes for Bayesian MARS models with many predictors

David J. Nott, Anthony Y. C. Kuk, Hiep Duc, 2005, Stat. Comput..

The ensemble Kalman filter is an ABC algorithm

Lucy Marshall, David J. Nott, Minh-Ngoc Tran, 2012, Stat. Comput..

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

Scott A. Sisson, J. Li, David J. Nott, 2015, 1504.04093.

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

David J. Nott, Nadja Klein, Minh-Ngoc Tran, 2019, J. Comput. Graph. Stat..

High-Dimensional Copula Variational Approximation Through Transformation

David J. Nott, Michael Stanley Smith, Ruben Loaiza-Maya, 2019 .

Approximation of Bayesian predictive p-values withregression ABC

Christopher C. Drovandi, Kerrie Mengersen, David J. Nott, 2018 .

Using History Matching for Prior Choice

Christopher C. Drovandi, Kerrie L. Mengersen, Michael Evans, 2016, Technometrics.

Likelihood-free inference in high dimensions with synthetic likelihood

Christopher C. Drovandi, Scott A. Sisson, David J. Nott, 2018, Comput. Stat. Data Anal..

Towards dynamic catchment modelling: a Bayesian hierarchical mixtures of experts framework

Lucy Marshall, David J. Nott, Ashish Sharma, 2007 .

Mixtures of experts for understanding model discrepancy in dynamic computer models

Shie-Yui Liong, Lucy Marshall, David J. Nott, 2014, Comput. Stat. Data Anal..

Detecting conflicting summary statistics in likelihood-free inference

Michael Evans, David J. Nott, Xueou Wang, 2020, Statistics and Computing.

Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices

David J. Nott, Nadja Klein, Michael Stanley Smith, 2020 .

Adaptive sampling for Bayesian variable selection

David J. Nott, Robert Kohn, R. Kohn, 2005 .

Multi-phase image modelling with excursion sets

David J. Nott, Richard J. Wilson, 2000, Signal Process..

A Bayesian hierarchical approach to model output statistics

David J. Nott, D. Nott, 2000 .

Functional regression approximate Bayesian computation for Gaussian process density estimation

Scott A. Sisson, David J. Nott, G. S. Rodrigues, 2014, Comput. Stat. Data Anal..

Robust Bayesian synthetic likelihood via a semi-parametric approach

David J. Nott, Christopher Drovandi, Ziwen An, 2018, Stat. Comput..

Accelerating Bayesian Synthetic Likelihood With the Graphical Lasso

Christopher C. Drovandi, David J. Nott, Ziwen An, 2019, Journal of Computational and Graphical Statistics.

Fast and Accurate Variational Inference for Models with Many Latent Variables

Peter J. Danaher, David J. Nott, Michael Stanley Smith, 2020 .

A note on approximating ABC‐MCMC using flexible classifiers

David J. Nott, Sanjay Chaudhuri, Kim Cuc Pham, 2014 .

Modeling the catchment via mixtures: Issues of model specification and validation

Ashish Sharma, Lucy Marshall, David J. Nott, 2005 .

Predictive performance of Dirichlet process shrinkage methods in linear regression

David J. Nott, 2008, Comput. Stat. Data Anal..

Variational Bayes with synthetic likelihood

Christopher C. Drovandi, Scott A. Sisson, David J. Nott, 2016, Statistics and Computing.

A Bayesian view of rainfall-runoff modelling: alternatives for parameter estimation, model comparison and hierarchical model development.

Ashish Sharma, Lucy Marshall, David J. Nott, 2005 .

A comparative study of Markov chain Monte Carlo methods for conceptual rainfall‐runoff modeling

Ashish Sharma, Lucy Marshall, David J. Nott, 2004 .

Identifying a Switch in the Catchment State through a Hierarchical Mixture of Experts model

Ashish Sharma, Lucy Marshall, David J. Nott, 2004 .

Parameter estimation for excursion set texture models

David J. Nott, Richard J. Wilson, D. Nott, 1997, Signal Process..

Variational inference for sparse spectrum Gaussian process regression

David J. Nott, Ajay Jasra, Linda S. L. Tan, 2013, Stat. Comput..

Checking for Prior-Data Conflict Using Prior-to-Posterior Divergences

Michael Evans, David J. Nott, Berthold-Georg Englert, 2016, Statistical Science.

Marginally-calibrated deep distributional regression

David J. Nott, Nadja Klein, Michael S Smith, 2019 .

A sign based loss approach to model selection in nonparametric regression

Jialiang Li, David J. Nott, D. Nott, 2010, Stat. Comput..

Conditionally structured variational Gaussian approximation with importance weights

David J. Nott, Linda S. L. Tan, Aishwarya Bhaskaran, 2019, Statistics and Computing.

Gaussian variational approximation with sparse precision matrices

David J. Nott, Linda S. L. Tan, D. Nott, 2016, Statistics and Computing.

Random Effects Models with Deep Neural Network Basis Functions: Methodology and Computation

David J. Nott, Robert Kohn, Minh-Ngoc Tran, 2017 .

Applications of Bayesian Methods in Conceptual Rainfall-runoff Modelling

Ashish Sharma, Lucy Marshall, David J. Nott, 2002 .

Computational Statistics and Data Analysis Bayesian Projection Approaches to Variable Selection in Generalized Linear Models

David J. Nott, Chenlei Leng, Chenlei Leng, 2022 .

Bayesian Inference Using Synthetic Likelihood: Asymptotics and Adjustments

David T. Frazier, David J. Nott, Robert Kohn, 2019, Journal of the American Statistical Association.

Hydrological model selection: A Bayesian alternative

Ashish Sharma, Lucy Marshall, David J. Nott, 2005 .

Using Prior Expansions for Prior-Data Conflict Checking

Michael Evans, David J. Nott, Berthold-Georg Englert, 2019, 1902.10393.

A general approach to heteroscedastic linear regression

David S. Leslie, David J. Nott, Robert Kohn, 2007, Stat. Comput..

Variational approximation for heteroscedastic linear models and matching pursuit algorithms

David J. Nott, Chenlei Leng, Minh-Ngoc Tran, 2010, Stat. Comput..

Variational Bayes With Intractable Likelihood

David J. Nott, Robert Kohn, Minh-Ngoc Tran, 2015, 1503.08621.

The predictive Lasso

David J. Nott, Chenlei Leng, Minh-Ngoc Tran, 2010, Stat. Comput..

Synthetic Likelihood in Misspecified Models: Consequences and Corrections

David T. Frazier, David J. Nott, Christopher Drovandi, 2021 .

A Semiautomatic Method for History Matching Using Sequential Monte Carlo

David J. Nott, Christopher Drovandi, Daniel E. Pagendam, 2021, SIAM/ASA J. Uncertain. Quantification.

Variational inference for cutting feedback in misspecified models

David J. Nott, Xuejun Yu, Michael Stanley Smith, 2021 .

Bayesian clustering using random effects models and predictive projections

David J. Nott, Yinan Mao, D. Nott, 2021, 2106.15847.

Variational Inference and Sparsity in High-Dimensional Deep Gaussian Mixture Models

David J. Nott, Nadja Klein, Lucas Kock, 2021 .

Confidence interval for the bootstrap P-value and sample size calculation of the bootstrap test

Jialiang Li, David J. Nott, D. Nott, 2009 .

Approximating the marginal likelihood using copula

David J. Nott, Mark Fielding, R. Kohn, 2008, 0810.5474.

Intra- and inter-individual genetic differences in gene expression

David J. Nott, Peter F.R. Little, Mark J. Cowley, 2008 .

An easy-to-use empirical likelihood ABC method

David J. Nott, Sanjay Chaudhuri, Kim Cuc Pham, 2018, 1810.01675.

Pairwise likelihood methods for inference in image models

David J. Nott, Tobias Rydén, T. Rydén, 1999 .

Meta-Analysis and Gene Set Enrichment Relative to ER Status Reveal Elevated Activity of MYC and E2F in the “Basal” Breast Cancer Subgroup

David J. Nott, Margaret Gardiner-Garden, Robert L. Sutherland, 2009, PloS one.

On a generalization of the Laplace approximation

David J. Nott, Mark Fielding, D. Nott, 2009 .

A pairwise likelihood approach to analyzing correlated binary data

David J. Nott, Anthony Y. C. Kuk, A. Kuk, 2000 .

Smoothing and Change Point Detection for Gamma Ray Count Data

David J. Nott, William T. M. Dunsmuir, W. Dunsmuir, 2003 .

Generalized likelihood uncertainty estimation (GLUE) and approximate Bayesian computation: What's the connection?

David J. Nott, Lucy Marshall, Jason Brown, 2012 .

Latin squares: critical sets and their lower bounds

Jennifer Seberry, David J. Nott, Diane Donovan, 1995 .

Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models

David J. Nott, Robert Kohn, R. Kohn, 2008 .

Intra- and inter-individual genetic differences in gene expression

David J. Nott, Mark J. Cowley, Peter F. R. Little, 2008, Mammalian Genome.

Generalized smooth finite mixtures

David J. Nott, Robert Kohn, Mattias Villani, 2012 .