Niels Richard Hansen

Artificial Intelligence · 2013–2018

A researcher in the Science corpus: 4 papers; trusts 48 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it; #13,762 of 35,345 by trust here, #23,731 by citations
• Statistics and Probability — 18 citations into it; #7,181 of 12,932 by trust here, #7,508 by citations
• Molecular Biology — 16 citations into it; #4,357 of 17,773 by trust here, #13,563 by citations
• Probabilistic & Bayesian Methods — 11 citations into it; #1,037 of 1,525 by trust here, #1,198 by citations
• Causal Inference & Discovery — 8 citations into it; #212 of 364 by trust here, #295 by citations
• Control and Systems Engineering — 6 citations into it
• Computational Theory and Mathematics — 5 citations into it; #9,887 of 12,182 by trust here, #10,901 by citations
• Computer Networks and Communications — 5 citations into it

Whom Niels Richard Hansen trusts overall

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

1. Vanessa Didelez
2. Thomas S. Richardson
3. Thomas Verma
4. Darren J. Wilkinson
5. Peter Spirtes
6. T. Schweder
7. James M. Varah
8. Nicolas J.-B. Brunel
9. Tomas Björk
10. Gerd Grubb

Who trusts Niels Richard Hansen overall

1. Søren Wengel Mogensen
2. Niklas Pfister
3. Jan Peters
4. Stefan Bauer
5. Daniel Malinsky
6. Stephan Bongers

Papers from Niels Richard Hansen's perspective

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

1. Causation, Prediction, and Search
2. Equivalence and Synthesis of Causal Models
3. Causality : Models , Reasoning , and Inference
4. Causation, prediction, and search
5. Ancestral graph Markov models
6. Regression Shrinkage and Selection via the Lasso
7. On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
8. Strong completeness and faithfulness in Bayesian networks
9. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
10. Regularization Paths for Generalized Linear Models via Coordinate Descent

Their papers

• Causal interpretation of stochastic differential equations (2013) — 61 citations
• Markov equivalence of marginalized local independence graphs (2018) — 50 citations
• Learning Large Scale Ordinary Differential Equation Systems (2017) — 12 citations
• Causal Learning for Partially Observed Stochastic Dynamical Systems (2018) — 42 citations