Pierre Ailliot

Global and Planetary Change · 2006–2018

A researcher in the Science corpus: 5 papers; trusts 16 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Global and Planetary Change — 29 citations into it; #3,686 of 10,225 by trust here, #8,506 by citations
• Atmospheric Science — 25 citations into it
• Artificial Intelligence — 12 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it
• Statistics and Probability — 9 citations into it; #9,964 of 12,932 by trust here, #12,077 by citations
• Environmental Engineering — 7 citations into it; #4,224 of 5,479 by trust here, #5,257 by citations

Whom Pierre Ailliot trusts overall

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

1. Arthur P. Dempster
2. Nan M. Laird
3. Haikady N. Nagaraja
4. Jeffrey L. Anderson
5. Donald B. Rubin
6. Christopher J. Paciorek
7. Randal Douc
8. Eckhard Liebscher
9. Marc Bocquet
10. G. Desroziers

Who trusts Pierre Ailliot overall

1. Pierre Pinson
2. Anastassia Baxevani
3. William Kleiber
4. Jan Lennartsson
5. Kenji Fukumizu
6. R. Katz

Papers from Pierre Ailliot's perspective

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

1. An Adaptive Ensemble Kalman Filter
2. An adaptive covariance inflation error correction algorithm for ensemble filters
3. Diagnosis of observation, background and analysis‐error statistics in observation space
4. Data Assimilation: The Ensemble Kalman Filter
5. Bias and data assimilation
6. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
7. Sequential Monte Carlo Methods in Practice
8. Combined Parameter and State Estimation in Simulation-Based Filtering (Sequential Monte Carlo Methods in Practice)
9. Ensemble Kalman filtering without the intrinsic need for inflation
10. Spatially and temporally varying adaptive covariance inflation for ensemble filters

Their papers

• L1-convergence of smoothing densities in non-parametric state space models (2008) — 6 citations
• Space–time modelling of precipitation by using a hidden Markov model and censored Gaussian distributions (2009) — 100 citations
• A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation (2018) — 88 citations
• Non-homogeneous hidden Markov-switching models for wind time series (2015) — 52 citations
• An autoregressive model with time‐varying coefficients for wind fields (2006) — 76 citations