Daniel Simpson

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Citations	12051	10190
h-index	38	34
i10-index	55	46

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Håvard RueKing Abdullah University of Science and TechnologyVerified email at kaust.edu.sa
Finn LindgrenUniversity of EdinburghVerified email at ed.ac.uk
Andrew GelmanProfessor of Statistics and Political Science, Columbia UniversityVerified email at stat.columbia.edu
Aki VehtariProfessor, Aalto UniversityVerified email at aalto.fi
Andrea RieblerDepartment of Mathematical Sciences, NTNU, Trondheim, NorwayVerified email at math.ntnu.no
Yuling YaoUT AustinVerified email at flatironinstitute.org
Geir-Arne FuglstadNorwegian University of Science and TechnologyVerified email at ntnu.no
Elias Teixeira KrainskiKAUSTVerified email at kaust.edu.sa
Michela CamelettiUniversità degli studi di BergamoVerified email at unibg.it
Ian W TurnerProfessor of Computational MathematicsVerified email at qut.edu.au
Mark GirolamiUniversity of CambridgeVerified email at cam.ac.uk
David BolinKing Abdullah University of Science and TechnologyVerified email at kaust.edu.sa
Xiangping HuNorwegian University of Science and TechnologyVerified email at ntnu.no
Anna Charisse FarrQueensland University of TechnologyVerified email at qut.edu.au
Alexander TereninCornell UniversityVerified email at cornell.edu
Kerrie MengersenQueensland University of TechnologyVerified email at qut.edu.au
Yu (Ryan) YueZicklin School of Business, Baruch College, CUNYVerified email at baruch.cuny.edu
Kelly SuttonHealth Economics Modelling Manger, Janssen AustraliaVerified email at its.jnj.com
Jo EidsvikMathematical sciences, NTNUVerified email at math.ntnu.no
Dann MalletProfessor of Mathematics Education, Queensland University of TechnologyVerified email at qut.edu.au

Daniel Simpson

Normal Computing

Verified email at normalcomputing.ai - Homepage

LLMs Computational Statistics Spatial Statistics Bayesian Statistics Numerical Linear Algebra


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Rank-normalization, folding, and localization: An improved R ̂ for assessing convergence of MCMC (with discussion) A Vehtari, A Gelman, D Simpson, B Carpenter, PC Bürkner Bayesian analysis 16 (2), 667-718, 2021	1366	2021
Penalising model component complexity: A principled, practical approach to constructing priors D Simpson, H Rue, A Riebler, TG Martins, SH Sørbye	1167	2017
Visualization in Bayesian workflow J Gabry, D Simpson, A Vehtari, M Betancourt, A Gelman Journal of the Royal Statistical Society Series A: Statistics in Society 182 …, 2019	947	2019
Bayesian computing with INLA: a review H Rue, A Riebler, SH Sørbye, JB Illian, DP Simpson, FK Lindgren Annual Review of Statistics and Its Application 4 (1), 395-421, 2017	748	2017
Bayesian computing with INLA: new features TG Martins, D Simpson, F Lindgren, H Rue Computational Statistics & Data Analysis 67, 68-83, 2013	670	2013
Using stacking to average Bayesian predictive distributions (with discussion) Y Yao, A Vehtari, D Simpson, A Gelman Bayesian Analysis 13 (3), 917-1003, 2018	610	2018
The prior can often only be understood in the context of the likelihood A Gelman, D Simpson, M Betancourt Entropy 19 (10), 555, 2017	570	2017
An intuitive Bayesian spatial model for disease mapping that accounts for scaling A Riebler, SH Sørbye, D Simpson, H Rue Statistical methods in medical research 25 (4), 1145-1165, 2016	449	2016
Constructing priors that penalize the complexity of Gaussian random fields GA Fuglstad, D Simpson, F Lindgren, H Rue Journal of the American Statistical Association 114 (525), 445-452, 2019	439	2019
Spatio-temporal modelling of particulate matter concentration through the SPDE approach M Cameletti, F Lindgren, DP Simpson, H Rue AStA Adv Stat Anal, Submitted, 2011	431	2011
Advanced spatial modeling with stochastic partial differential equations using R and INLA E Krainski, V Gómez-Rubio, H Bakka, A Lenzi, D Castro-Camilo, ... Chapman and Hall/CRC, 2018	385*	2018
Spatial modeling with R‐INLA: A review H Bakka, H Rue, GA Fuglstad, A Riebler, D Bolin, J Illian, E Krainski, ... Wiley Interdisciplinary Reviews: Computational Statistics 10 (6), e1443, 2018	374	2018
Pareto smoothed importance sampling A Vehtari, D Simpson, A Gelman, Y Yao, J Gabry arXiv preprint arXiv:1507.02646, 2015	360	2015
Bayesian workflow A Gelman, A Vehtari, D Simpson, CC Margossian, B Carpenter, Y Yao, ... arXiv preprint arXiv:2011.01808, 2020	350	2020
Going off grid: Computationally efficient inference for log-Gaussian Cox processes D Simpson, JB Illian, F Lindgren, SH Sørbye, H Rue Biometrika 103 (1), 49-70, 2016	273	2016
Validating Bayesian inference algorithms with simulation-based calibration S Talts, M Betancourt, D Simpson, A Vehtari, A Gelman arXiv preprint arXiv:1804.06788, 2018	251	2018
Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment G Shaddick, ML Thomas, H Amini, D Broday, A Cohen, J Frostad, A Green, ... Environmental science & technology 52 (16), 9069-9078, 2018	224	2018
Yes, but did it work?: Evaluating variational inference Y Yao, A Vehtari, D Simpson, A Gelman International Conference on Machine Learning, 5581-5590, 2018	188	2018
Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan M Morris, K Wheeler-Martin, D Simpson, SJ Mooney, A Gelman, ... Spatial and spatio-temporal epidemiology 31, 100301, 2019	173	2019
On Russian roulette estimates for Bayesian inference with doubly-intractable likelihoods AM Lyne, M Girolami, Y Atchadé, H Strathmann, D Simpson	156*	2015

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