Anthony J. Jakeman
Statistics and Probability · 2002–2020
A researcher in the Science corpus: 13 papers; trusts 11 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 81 citations into it
- Global and Planetary Change — 61 citations into it; #3,674 of 10,225 by trust here, #4,309 by citations
- Water Science and Technology — 60 citations into it; #142 of 1,711 by trust here, #239 by citations
- Environmental Engineering — 49 citations into it; #3,879 of 5,479 by trust here, #3,797 by citations
- Artificial Intelligence — 43 citations into it; #28,093 of 35,345 by trust here, #26,682 by citations
- Management Science and Operations Research — 42 citations into it
- Civil and Structural Engineering — 25 citations into it
- Ocean Engineering — 22 citations into it; #839 of 3,193 by trust here, #155 by citations
Whom Anthony J. Jakeman trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Andrea Saltelli
- J. P. van der Sluijs
- Kwok-Wing Chau
- Ian D. Watson
- Silvio Funtowicz
- Bruno Sudret
- Thorsten Wagener
- Trevor J. Hastie
- Richard S. Sutton
- Ilya M. Sobol
Who trusts Anthony J. Jakeman overall
- B. Blocken
- Jens Christian Refsgaard
- Carlo Gualtieri
- Holger R. Maier
- Alexey A. Voinov
- Julien J. Harou
- J. P. van der Sluijs
- Xiaomeng Song
- Yuqiong Liu
- Sheen Mclean Cabaneros
Papers from Anthony J. Jakeman's perspective
Ordered by how strongly the researchers Anthony J. Jakeman trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Reinforcement Learning: An Introduction
- Random Forests
- Genetic Algorithms in Search Optimization and Machine Learning
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- Probabilistic sensitivity analysis of complex models: a Bayesian approach
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Making best use of model evaluations to compute sensitivity indices
- Towards reduced uncertainty in conceptual rainfall‐runoff modelling: dynamic identifiability analysis
- A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Their papers
- Characterising performance of environmental models (2013) — 1,458 citations
- The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020) — 552 citations
- Flood inundation modelling: A review of methods, recent advances and uncertainty analysis (2017) — 1,199 citations
- A review of foundational methods for checking the structural identifiability of models: Results for rainfall-runoff (2015) — 67 citations
- Selecting among five common modelling approaches for integrated environmental assessment and management (2013) — 766 citations
- Addressing ten questions about conceptual rainfall–runoff models with global sensitivity analyses in R (2013) — 183 citations
- Environmental decision support systems (EDSS) development - Challenges and best practices (2011) — 314 citations
- Assessing the impact of land use change on hydrology by ensemble modelling(LUCHEM) II: ensemble combinations and predictions (2009) — 147 citations
- Artificial Intelligence techniques: An introduction to their use for modelling environmental systems (2008) — 297 citations
- Ten iterative steps in development and evaluation of environmental models (2006) — 1,164 citations
- A review of erosion and sediment transport models (2003) — 1,183 citations
- Integrated assessment and modelling: features, principles and examples for catchment management (2003) — 454 citations
- Progress in integrated assessment and modelling (2002) — 303 citations