Elmar Plischke
Statistics and Probability · 2013–2020
A researcher in the Science corpus: 3 papers; trusts 16 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 96 citations into it; #7,129 of 12,932 by trust here, #4,371 by citations
- Computational Theory and Mathematics — 33 citations into it; #6,422 of 12,182 by trust here, #3,924 by citations
- Management Science and Operations Research — 28 citations into it
- Civil and Structural Engineering — 24 citations into it
- Artificial Intelligence — 22 citations into it
- Mechanics of Materials — 19 citations into it
- Statistical and Nonlinear Physics — 12 citations into it
- Safety, Risk, Reliability and Quality — 7 citations into it
Whom Elmar Plischke trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Andrea Saltelli
- Bruno Sudret
- Jack P. C. Kleijnen
- Stefano Tarantola
- Jeremy E. Oakley
- Jon C. Helton
- Alan E. Gelfand
- Luc Devroye
- Alison L. Gibbs
- Ilya M. Sobol
Who trusts Elmar Plischke overall
Papers from Elmar Plischke's perspective
Ordered by how strongly the researchers Elmar Plischke trusts endorse each paper. Same corpus, a different order for every researcher.
- Sensitivity Estimates for Nonlinear Mathematical Models
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code
- Probabilistic sensitivity analysis of complex models: a Bayesian approach
- Random Forests
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations (SIAM Journal on Scientific Computing)
- Making best use of model evaluations to compute sensitivity indices
- Calculations of Sobol indices for the Gaussian process metamodel (Reliability Engineering & System Safety)
- Design and Modeling for Computer Experiments
- ImageNet classification with deep convolutional neural networks
Their papers
- Sensitivity analysis: A review of recent advances (2016) — 1,059 citations
- Global sensitivity measures from given data (2013) — 398 citations
- The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020) — 552 citations