James Martin
Statistics and Probability · 2012–2013
A researcher in the Science corpus: 2 papers; trusts 18 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 22 citations into it; #11,278 of 12,932 by trust here, #7,508 by citations
- Artificial Intelligence — 16 citations into it; #26,914 of 35,345 by trust here, #22,463 by citations
- Probabilistic & Bayesian Methods — 8 citations into it
- Computational Theory and Mathematics — 7 citations into it
- Applied Mathematics — 6 citations into it
- Mathematical Physics — 6 citations into it
- Statistical and Nonlinear Physics — 5 citations into it
Whom James Martin trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Youssef M. Marzouk
- Albert Tarantola
- Radford M. Neal
- Andrew M. Stuart
- Mark A. Girolami
- Gareth O. Roberts
- F. Liang
- Jingbo Wang
- Habib N. Najm
- Håvard Rue
Who trusts James Martin overall
- Tarek A. El-Moselhy
- Benjamin Peherstorfer
- Valdemar Melicher
- Youssef M. Marzouk
- Sinho Chewi
- Karen E. Willcox
- Tom Haber
Papers from James Martin's perspective
Ordered by how strongly the researchers James Martin trusts endorse each paper. Same corpus, a different order for every researcher.
- Inverse problem theory - and methods for model parameter estimation
- Monte Carlo Statistical Methods
- Riemann manifold Langevin and Hamiltonian Monte Carlo methods
- Sampling-Based Approaches to Calculating Marginal Densities
- Statistical and Computational Inverse Problems (Technometrics)
- Optimal scaling for various Metropolis-Hastings algorithms
- Gaussian Markov Random Fields: Theory and Applications
- Stochastic spectral methods for efficient Bayesian solution of inverse problems (Journal of Computational Physics)
- Using Bayesian statistics in the estimation of heat source in radiation
- Dimensionality reduction and polynomial chaos acceleration of Bayesian inference in inverse problems (Journal of Computational Physics)
Their papers
- A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion (2012) — 469 citations
- A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems Part I: The Linearized Case, with Application to Global Seismic Inversion (2013) — 439 citations