Daniel P. Simpson

Statistics and Probability · 2010–2020

A researcher in the Science corpus: 15 papers; trusts 88 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 190 citations into it; #3,201 of 12,932 by trust here, #2,214 by citations
• Artificial Intelligence — 128 citations into it; #7,668 of 35,345 by trust here, #9,760 by citations
• Economics and Econometrics — 58 citations into it
• Environmental Engineering — 48 citations into it; #2,060 of 5,479 by trust here, #2,438 by citations
• Probabilistic & Bayesian Methods — 41 citations into it
• Management Science and Operations Research — 25 citations into it; #11,557 of 11,946 by trust here, #6,563 by citations
• Global and Planetary Change — 17 citations into it; #7,703 of 10,225 by trust here, #8,506 by citations
• Applied Mathematics — 14 citations into it

Whom Daniel P. Simpson trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Håvard Rue
2. Andrew Gelman
3. Finn Lindgren
4. Trevor J. Hastie
5. Donald B. Rubin
6. Leonhard Held
7. Michael Betancourt
8. Christian P. Robert
9. Robert Tibshirani
10. James O. Berger

Who trusts Daniel P. Simpson overall

1. Håvard Rue
2. Andrea Riebler
3. Andrew Gelman
4. Aki Vehtari
5. Finn Lindgren
6. H. Bakka
7. Christian P. Robert
8. Yuling Yao
9. Thomas Opitz
10. Jonathan Koh

Papers from Daniel P. Simpson's perspective

Ordered by how strongly the researchers Daniel P. Simpson trusts endorse each paper. Same corpus, a different order for every researcher.

1. Sampling-Based Approaches to Calculating Marginal Densities
2. Bayesian measures of model complexity and fit
3. Regression Shrinkage and Selection via the Lasso
4. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
5. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)
6. Spatial Interaction and the Statistical Analysis of Lattice Systems
7. Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors
8. Gaussian Markov Random Fields: Theory and Applications
9. Pattern Recognition and Machine Learning
10. Estimating the Dimension of a Model

Their papers

• Validating Bayesian Inference Algorithms with Simulation-Based Calibration (2018) — 386 citations
• Bayesian Computing with INLA: A Review (2016) — 746 citations
• Using Stacking to Average Bayesian Predictive Distributions (with Discussion) (2017) — 424 citations
• The Prior Can Often Only Be Understood in the Context of the Likelihood (2017) — 481 citations
• Rank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (with Discussion) (2019) — 1,440 citations
• Bayesian computing with INLA: New features (2012) — 626 citations
• An intuitive Bayesian spatial model for disease mapping that accounts for scaling (2016) — 496 citations
• A restarted Lanczos approximation to functions of a symmetric matrix (2010) — 59 citations
• Spatial modelling with R-INLA: A review (2018) — 401 citations
• Bayesian Workflow (2020) — 344 citations
• Gaydar and the Fallacy of Decontextualized Measurement (2018) — 16 citations
• Visualization in Bayesian workflow (2017) — 981 citations
• Constructing Priors that Penalize the Complexity of Gaussian Random Fields (2015) — 413 citations
• Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors (2014) — 1,120 citations
• Spatio-temporal modeling of particulate matter concentration through the SPDE approach (2012) — 406 citations