Bruno Sansó

Statistics and Probability · 2001–2010

A researcher in the Science corpus: 6 papers; trusts 20 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 22 citations into it
• Artificial Intelligence — 15 citations into it; #13,118 of 35,345 by trust here, #31,361 by citations
• Environmental Engineering — 10 citations into it; #1,095 of 5,479 by trust here, #1,260 by citations
• Global and Planetary Change — 8 citations into it; #4,805 of 10,225 by trust here, #6,762 by citations
• Economics and Econometrics — 7 citations into it; #3,846 of 23,481 by trust here, #6,966 by citations
• Finance — 6 citations into it; #4,083 of 10,518 by trust here, #5,938 by citations
• Applied Mathematics — 5 citations into it; #2,629 of 7,620 by trust here, #5,850 by citations

Whom Bruno Sansó trusts overall

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

1. D. Higdon
2. Stuart Coles
3. Radford M. Neal
4. Montserrat Fuentes
5. S. Ejaz Ahmed
6. Athanasios Kottas
7. Andrew Gelman
8. A. Brix
9. Jesper Møller
10. Peter Müller

Who trusts Bruno Sansó overall

1. Matt Taddy
2. Noel Cressie
3. Flávio B. Gonçalves
4. Ryan P. Adams
5. Jorge Alberto Achcar
6. Mehmet Can Vuran
7. Dani Gamerman
8. Athanasios Kottas
9. James O. Berger
10. María Blanca Palacios

Papers from Bruno Sansó's perspective

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

1. Non-Stationary Spatial Modeling
2. Inference from Iterative Simulation Using Multiple Sequences
3. Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems
4. Space and Space-Time Modeling using Process Convolutions
5. An Introduction to Statistical Modeling of Extreme Values
6. Bayesian Density Estimation and Inference Using Mixtures
7. A process-convolution approach to modelling temperatures in the North Atlantic Ocean
8. Interpolation of spatial data
9. Markov Chain Sampling Methods for Dirichlet Process Mixture Models
10. A fully probabilistic approach to extreme rainfall modeling

Their papers

• Bayesian mixture modeling for spatial Poisson process intensities, with applications to extreme value analysis (2007) — 121 citations
• Objective Bayesian Analysis of Spatially Correlated Data (2001) — 550 citations
• Time-varying models for extreme values (2007) — 101 citations
• Biological communities in San Francisco Bay track large‐scale climate forcing over the North Pacific (2010) — 103 citations
• A Spatio-Temporal Model for Mean, Anomaly, and Trend Fields of North Atlantic Sea Surface Temperature (2009) — 73 citations
• Dynamic models for spatiotemporal data (2001) — 236 citations