Jonathan Koh

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 Jonathan Koh 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
  10. Thomas Opitz

Papers from Jonathan Koh's perspective

Ordered by how strongly the researchers Jonathan Koh 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. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
  8. Spatial Point Patterns: Methodology and Applications with R
  9. INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles (Extremes)
  10. Geostatistical inference under preferential sampling

Their papers