James M. Hyman

Artificial Intelligence · 2001–2020

A researcher in the Science corpus: 9 papers; trusts 21 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it; #24,059 of 35,345 by trust here, #23,077 by citations
• Global and Planetary Change — 14 citations into it; #3,149 of 10,225 by trust here, #3,676 by citations
• Computational Theory and Mathematics — 13 citations into it; #4,638 of 12,182 by trust here, #1,700 by citations
• Water Science and Technology — 13 citations into it; #404 of 1,711 by trust here, #575 by citations
• Statistics and Probability — 12 citations into it; #4,998 of 12,932 by trust here, #2,407 by citations
• Probabilistic & Bayesian Methods — 8 citations into it
• Modeling and Simulation — 6 citations into it; #1,852 of 2,105 by trust here, #1,128 by citations
• Mathematical Physics — 5 citations into it

Whom James M. Hyman trusts overall

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

1. Nakul Chitnis
2. Kalyanmoy Deb
3. Nikolaus Hansen
4. Heikki Haario
5. C. T. ter Braak
6. Andrew Gelman
7. Anne Auger
8. Qingyun Duan
9. Ponnuthurai Nagaratnam Suganthan
10. Jing J. Liang

Who trusts James M. Hyman overall

1. Jasper A. Vrugt
2. Franco Brezzi
3. Steven L. Brunton
4. Konstantin Lipnikov
5. Runhild A. Klausen
6. Eric Laloy
7. Ioannis G. Kevrekidis
8. Mojtaba Sadegh
9. Rowan Cockett
10. J. Nathan Kutz

Papers from James M. Hyman's perspective

Ordered by how strongly the researchers James M. Hyman trusts endorse each paper. Same corpus, a different order for every researcher.

1. Inference from Iterative Simulation Using Multiple Sequences
2. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
3. A fast and elitist multiobjective genetic algorithm: NSGA-II
4. Adaptive proposal distribution for random walk Metropolis algorithm
5. Multi-objective optimization using evolutionary algorithms
6. Sampling-Based Approaches to Calculating Marginal Densities
7. Effective and efficient global optimization for conceptual rainfall‐runoff models
8. Optimal scaling for various Metropolis-Hastings algorithms
9. Monte Carlo Statistical Methods
10. No free lunch theorems for optimization

Their papers

• Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces (2009) — 350 citations
• Equation-Free, Coarse-Grained Multiscale Computation: Enabling Mocroscopic Simulators to Perform System-Level Analysis (2003) — 832 citations
• Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling (2009) — 1,086 citations
• Fourth- and sixth-order conservative finite difference approximations of the divergence and gradient (2001) — 62 citations
• Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020 (2020) — 650 citations
• Determining Important Parameters in the Spread of Malaria Through the Sensitivity Analysis of a Mathematical Model (2008) — 1,707 citations
• Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation (2008) — 845 citations
• Bifurcation Analysis of a Mathematical Model for Malaria Transmission (2006) — 487 citations
• Mimetic Finite Difference Methods for Diffusion Equations (2002) — 155 citations