Sara Martino

Statistics and Probability · 2007–2014

A researcher in the Science corpus: 4 papers; trusts 21 researchers and is trusted by 85 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 51 citations into it; #738 of 12,932 by trust here, #907 by citations
• Environmental Engineering — 33 citations into it; #4,654 of 5,479 by trust here, #3,944 by citations
• Artificial Intelligence — 29 citations into it; #2,948 of 35,345 by trust here, #5,834 by citations
• Economics and Econometrics — 24 citations into it; #20,524 of 23,481 by trust here, #22,128 by citations
• Applied Mathematics — 13 citations into it
• Global and Planetary Change — 11 citations into it; #8,218 of 10,225 by trust here, #9,714 by citations
• Probabilistic & Bayesian Methods — 10 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Sara Martino trusts overall

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

1. Håvard Rue
2. Jesper Møller
3. Tilmann Gneiting
4. Tom Minka
5. Eric R. Ziegel
6. David J. Spiegelhalter
7. Finn Lindgren
8. Gareth O. Roberts
9. Adelchi Azzalini
10. Peter J. Diggle

Who trusts Sara Martino overall

1. Jian Qing Shi
2. Finn Lindgren
3. Paul H. C. Eilers
4. Chong Wang
5. Håvard Rue
6. Daniel P. Simpson
7. Andrew Gelman
8. Chong You
9. Jay M. Ver Hoef
10. Aki Vehtari

Papers from Sara Martino's perspective

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

1. Gaussian Markov Random Fields: Theory and Applications
2. Hierarchical Modeling and Analysis for Spatial Data
3. Expectation Propagation for approximate Bayesian inference
4. Statistical Analysis of Spatial Point Patterns (Technometrics)
5. Statistical Analysis and Modelling of Spatial Point Patterns
6. Nonparametric Estimation of Nonstationary Spatial Covariance Structure
7. Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors
8. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach
9. Spatial modelling using a new class of nonstationary covariance functions (Environmetrics)
10. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)

Their papers

• Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (2009) — 5,252 citations
• Estimation of a non-stationary model for annual precipitation in southern Norway using replicates of the spatial field (2014) — 47 citations
• Fitting complex ecological point process models with integrated nested Laplace approximation (2013) — 103 citations
• Approximate Bayesian Inference for Latent Gaussian Models (2007) — 314 citations