Jean-Luc Dupuy

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Jean-Luc Dupuy trusts overall

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

  1. Håvard Rue
  2. Finn Lindgren
  3. Sumio Watanabe
  4. Tom Fawcett
  5. Adrian Baddeley
  6. Daniel P. Simpson
  7. Tilmann Gneiting
  8. J. Matheson
  9. Paul Northrop

Papers from Jean-Luc Dupuy's perspective

Ordered by how strongly the researchers Jean-Luc Dupuy trusts endorse each paper. Same corpus, a different order for every researcher.

  1. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach
  2. Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors
  3. Bayesian Spatial Modelling with R-INLA
  4. Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory
  5. Bayesian Computing with INLA: A Review
  6. Constructing Priors that Penalize the Complexity of Gaussian Random Fields (Journal of the American Statistical Association)
  7. Spatial Point Patterns: Methodology and Applications with R
  8. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
  9. INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles (Extremes)
  10. Geostatistical inference under preferential sampling

Their papers