Daniel P. Simpson

发表

Parameter estimation in high dimensional Gaussian distributions

Jo Eidsvik, Erlend Aune, Daniel P. Simpson, 2011, Stat. Comput..

Fast sampling from a Gaussian Markov random ﬁeld using Krylov subspace approaches

A. Pettitt, I. Turner, Daniel P. Simpson, 2008 .

Krylov subspace methods for approximating functions of symmetric positive definite matrices with applications to applied statistics and anomalous diffusion

Daniel P. Simpson, 2008 .

Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors

Thiago G. Martins, H. Rue, Daniel P. Simpson, 2014, 1403.4630.

On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoods

Yves F. Atchad'e, M. Girolami, Heiko Strathmann, 2013, 1306.4032.

A restarted Lanczos approximation to functions of a symmetric matrix

I. Turner, M. Ilic, Daniel P. Simpson, 2010 .

In order to make spatial statistics computationally feasible, we need to forget about the covariance function

H. Rue, F. Lindgren, Daniel P. Simpson, 2012 .

Spatio-temporal modeling of particulate matter concentration through the SPDE approach

H. Rue, F. Lindgren, Daniel P. Simpson, 2012, AStA Advances in Statistical Analysis.

Yes, but Did It Work?: Evaluating Variational Inference

Aki Vehtari, Andrew Gelman, Daniel Simpson, 2018, ICML.

Exploring a New Class of Non-stationary Spatial Gaussian Random Fields with Varying Local Anisotropy

F. Lindgren, H. Rue, Daniel P. Simpson, 2013, 1304.6949.

Going off grid: computationally efficient inference for log-Gaussian Cox processes

Haavard Rue, Finn Lindgren, Daniel Simpson, 2016 .

The prior can generally only be understood in the context of the likelihood

A. Gelman, M. Betancourt, Daniel P. Simpson, 2017 .

NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET Parameter Estimation in High Dimensional Gaussian Distributions

Erlend Aune, Daniel P. Simpson, J. Eidsvik, 2012 .

Rank-normalization, folding, and localization: An improved R-hat for assessing convergence Rank-Normalization, Folding, and Localization: An Improved (cid:2) R for Assessing Convergence of MCMC An assessing for assessing An improved (cid:2) R for assessing convergence of MCMC

A. Gelman, Aki Vehtari, B. Carpenter, 2020 .

Discussion on the meeting on ‘Data visualization’

P. Diggle, A. Gelman, A. Atkinson, 2019, Journal of the Royal Statistical Society: Series A (Statistics in Society).

Rank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (with Discussion)

Aki Vehtari, Bob Carpenter, Daniel Simpson, 2019, Bayesian Analysis.

Gaydar and the Fallacy of Decontextualized Measurement

Daniel Simpson, Andrew Gelman, A. Gelman, 2018 .

Careful prior specification avoids incautious inference for log‐Gaussian Cox point processes

Janine B. Illian, Sigrunn H. Sorbye, David Burslem, 2017, Journal of the Royal Statistical Society: Series C (Applied Statistics).

The use of systems of stochastic PDEs as priors for multivariate models with discrete structures

Erlend Aune, Daniel P. Simpson, 2012, 1208.1717.

The MCMC split sampler: A block Gibbs sampling scheme for latent Gaussian models

Daniel P. Simpson, B. Hrafnkelsson, 'Oli P'all Geirsson, 2015, 1506.06285.

Specifying Gaussian Markov Random Fields with Incomplete Orthogonal Factorization using Givens Rotations

H. Rue, Daniel P. Simpson, Xiangping Hu, 2013, 1307.1368.

The Integrated nested Laplace approximation for fitting models with multivariate response

F. Lindgren, Daniel P. Simpson, D. Conesa, 2019, 1907.04059.

Bayesian Computing with INLA: A Review

H. Rue, F. Lindgren, H. Rue, 2016, 1604.00860.

Non-stationary Spatial Modelling with Applications to Spatial Prediction of Precipitation

F. Lindgren, H. Rue, Daniel P. Simpson, 2013, 1306.0408.

Beyond the Valley of the Covariance Function

Haavard Rue, Finn Lindgren, Daniel Simpson, 2015, 1507.08383.

Using Stacking to Average Bayesian Predictive Distributions (with Discussion)

A. Gelman, Aki Vehtari, Daniel P. Simpson, 2017, Bayesian Analysis.

Discussion to ’ Uncertainty Quantification for the Horseshoe ’ by Stéphanie van der Pas , Botond Szabó , and Aad van der Vaart

Aki Vehtari, M. Betancourt, Daniel P. Simpson, 2017 .

Spatial Modeling, with Application to Complex Survey Data: Discussion of "Model-based Geostatistics for Prevalence Mapping in Low-Resource Settings", by Diggle and Giorgi

J. Wakefield, Daniel P. Simpson, J. Godwin, 2016, 1608.03769.

Edinburgh Explorer Bayesian Computing with INLA: A Review

H. Rue, F. Lindgren, Daniel P. Simpson, 2018 .

The use of systems of stochastic PDEs as priors for multivariate models with discrete structures

Erlend Aune, Daniel P. Simpson, 2012, 1208.1717.

Interpretable Priors for Hyperparameters for Gaussian Random Fields

H. Rue, F. Lindgren, Daniel P. Simpson, 2015 .

Playing Russian Roulette with Intractable Likelihoods

M. Girolami, Heiko Strathmann, Y. Atchadé, 2013 .

LGM Split Sampler: An Efﬁcient Sampling Scheme for Latent Gaussian Models

Daniel P. Simpson, B. Hrafnkelsson, Ó. P. Geirsson, 2020 .

The Integrated Nested Laplace Approximation for Fitting Dirichlet Regression Models

F. Lindgren, Daniel P. Simpson, D. Conesa, 2019, Journal of Computational and Graphical Statistics.

TEKNISK-NATURVITENSKAPELIGE UNIVERSITET Bayesian computing with INLA : new features by

Thiago G. Martins, F. Lindgren, Daniel P. Simpson, 2012 .

Spatio-temporal modeling of particulate matter concentration through the SPDE approach

H. Rue, F. Lindgren, Daniel P. Simpson, 2012, AStA Advances in Statistical Analysis.

Bayesian Workflow.

Aki Vehtari, Bob Carpenter, Jonah Gabry, 2020, 2011.01808.

Constructing Priors that Penalize the Complexity of Gaussian Random Fields

H. Rue, F. Lindgren, Daniel P. Simpson, 2015, Journal of the American Statistical Association.

You Just Keep on Pushing My Love over the Borderline: A Rejoinder

Thiago G. Martins, H. Rue, Daniel P. Simpson, 2017 .

Discussion on “Spatial prediction in the presence of positional error”

Finn Lindgren, Daniel Simpson, Håvard Rue, 2011 .

Combining Opinions for Use in Bayesian Networks: A Measurement Error Approach

Kerrie Mengersen, Daniel Simpson, Fabrizio Ruggeri, 2014, International Statistical Review.

Spatial modelling with R-INLA: A review

Haavard Rue, David Bolin, Finn Lindgren, 2018, 1802.06350.

Constructing Priors that Penalize the Complexity of Gaussian Random Fields

Haavard Rue, Finn Lindgren, Daniel Simpson, 2015, Journal of the American Statistical Association.

Does non-stationary spatial data always require non-stationary random fields?

Haavard Rue, Finn Lindgren, Daniel Simpson, 2014 .

Computationally efficient spatial modeling of annual maximum 24‐h precipitation on a fine grid

Daniel P. Simpson, B. Hrafnkelsson, Ó. P. Geirsson, 2014, 1405.6947.

Think continuous: Markovian Gaussian models in spatial statistics

Haavard Rue, Finn Lindgren, Daniel Simpson, 2011, 1110.6796.

The experiment is just as important as the likelihood in understanding the prior : A cautionary note on robust cognitive modelling

Andrew Gelman, Lauren Kennedy, Daniel Simpson, 2019 .

Do We Need Non-Stationarity in Spatial Models?

H. Rue, Daniel P. Simpson, Geir-Arne Fuglstad, 2014 .

Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA

Finn Lindgren, Virgilio Gómez-Rubio, Håvard Rue, 2018 .

Bayesian Computing with INLA: A Review

Haavard Rue, Daniel P. Simpson, Andrea Riebler, 2016, 1604.00860.

Visualization in Bayesian workflow

Aki Vehtari, Andrew Gelman, Jonah Gabry, 2017, Journal of the Royal Statistical Society: Series A (Statistics in Society).

The Prior Can Often Only Be Understood in the Context of the Likelihood

Andrew Gelman, Daniel Simpson, Michael Betancourt, 2017, Entropy.

An intuitive Bayesian spatial model for disease mapping that accounts for scaling

Andrea Riebler, Daniel Simpson, Håvard Rue, 2016, Statistical methods in medical research.

Using Stacking to Average Bayesian Predictive Distributions (with Discussion)

Aki Vehtari, Andrew Gelman, Daniel Simpson, 2017, Bayesian Analysis.

Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan.

Stephen J Mooney, Andrew Gelman, Katherine Wheeler-Martin, 2019, Spatial and spatio-temporal epidemiology.

Validating Bayesian Inference Algorithms with Simulation-Based Calibration

Aki Vehtari, Andrew Gelman, Daniel Simpson, 2018, 1804.06788.

Non-stationary Gaussian models with physical barriers

Haavard Rue, Daniel Simpson, Jarno Vanhatalo, 2016, Spatial Statistics.

Bayesian computing with INLA: New features

Finn Lindgren, Daniel Simpson, Håvard Rue, 2012, Comput. Stat. Data Anal..

Improving multilevel regression and poststratification with structured priors.

Daniel Simpson, Andrew Gelman, Lauren Kennedy, 2019, Bayesian analysis.

Asynchronous Gibbs Sampling

Daniel Simpson, David Draper, Alexander Terenin, 2015, AISTATS.

A Bayesian General Linear Modeling Approach to Slice-wise fMRI Data Analysis

Martin A. Lindquist, David Bolin, Finn Lindgren, 2014 .

Asynchronous Distributed Gibbs Sampling

D. Draper, Daniel P. Simpson, Alexander Terenin, 2015 .

Bayesian Adaptive Smoothing Spline using Stochastic Differential Equations

Haavard Rue, Finn Lindgren, Daniel Simpson, 2012, 1209.2013.

Treatment effect estimation with Multilevel Regression and Poststratification

Daniel Simpson, Lauren Kennedy, Yuxiang Gao, 2021, 2102.10003.

Limitations of “Limitations of Bayesian Leave-one-out Cross-Validation for Model Selection”

Aki Vehtari, Andrew Gelman, Yuling Yao, 2018, Computational Brain & Behavior.

A generalised matrix transfer technique for the numerical solution of fractional-in-space partial differential equations

I. Turner, M. Ilic, Daniel P. Simpson, 2009 .

Accounting for physical barriers in species distribution modeling with non-stationary spatial random effects

Daniel Simpson, Jarno Vanhatalo, Håvard Rue, 2016 .

Norges Teknisk-naturvitenskapelige Universitet Multivariate Gaussian Random Fields Using Systems of Stochastic Partial Differential Equations Multivariate Gaussian Random Fields Using Systems of Stochastic Partial Differential Equations

Haavard Rue, Finn Lindgren, Daniel Simpson, 2022 .

Using sex and gender in survey adjustment

A. Gelman, Daniel P. Simpson, J. Teitler, 2020, 2009.14401.

He, she, they: Using sex and gender in survey adjustment

Daniel P. Simpson, J. Teitler, Yajun Jia, 2022 .

Multivariate Gaussian Random Fields with Oscillating Covariance Functions using Systems of Stochastic Partial Differential Equations

Haavard Rue, Finn Lindgren, Daniel Simpson, 2013, 1307.1384.

The use of systems of stochastic PDEs as priors in seismic AVA inversion

Erlend Aune, Daniel P. Simpson, 2012 .

Spatial Modelling of Temperature and Humidity using Systems of Stochastic Partial Differential Equations

Haavard Rue, Daniel Simpson, Sara Martino, 2013, 1307.1402.

Thermodynamic Linear Algebra

Patrick J. Coles, Thomas Dybdahl Ahle, G. Crooks, 2023, ArXiv.

Scalable iterative methods for sampling from massive Gaussian random vectors

A. Pettitt, I. Turner, Daniel P. Simpson, 2013, 1312.1476.

Fast approximate inference with INLA: the past, the present and the future

F. Lindgren, H. Rue, Daniel P. Simpson, 2011, 1105.2982.

LGM Split Sampler: An Efficient MCMC Sampling Scheme for Latent Gaussian Models

Daniel Simpson, Birgir Hrafnkelsson, Óli Páll Geirsson, 2020 .

Spatio-temporal modeling of particulate matter concentration through the SPDE approach

H. Rue, F. Lindgren, Daniel P. Simpson, 2012, AStA Advances in Statistical Analysis.

Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond

Aki Vehtari, Daniel Simpson, Charles C. Margossian, 2020, NeurIPS.

Constructing Priors that Penalize the Complexity of Gaussian Random Fields

H. Rue, F. Lindgren, Daniel P. Simpson, 2015, Journal of the American Statistical Association.

Norges Teknisk-naturvitenskapelige Universitet Going off Grid: Computationally Efficient Inference for Log-gaussian Cox Processes Going off Grid: Computationally Efficient Inference for Log-gaussian Cox Processes

H. Rue, F. Lindgren, Daniel P. Simpson, 2022 .

Cross-Validatory Model Selection for Bayesian Autoregressions with Exogenous Regressors

Catherine S. Forbes, Aki Vehtari, Daniel P. Simpson, 2023, Bayesian Analysis.

The Integrated nested Laplace approximation for fitting models with multivariate response

F. Lindgren, Daniel P. Simpson, D. Conesa, 2019 .

The Experiment is just as Important as the Likelihood in Understanding the Prior: a Cautionary Note on Robust Cognitive Modeling

A. Gelman, Daniel P. Simpson, Lauren Kennedy, 2019, Computational Brain & Behavior.

On the Convergence of the Escalator Boxcar Train

Daniel Simpson, Åke Brännström, Linus Carlsson, 2012, SIAM J. Numer. Anal..

Gaydar and the fallacy of objective measurement ∗

A. Gelman, Daniel P. Simpson, Greggor Mattson, 2018 .

Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan.

A. Gelman, Daniel P. Simpson, C. DiMaggio, 2019, Spatial and spatio-temporal epidemiology.

Hamiltonian Monte Carlo using an embedded Laplace approximation

Daniel P. Simpson, Raj Agrawal, A. Vehtari, 2020, 2004.12550.