S. Aeschbacher
Statistics and Probability · 2012
A researcher in the Science corpus: 1 paper; trusts 14 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 19 citations into it; #5,386 of 12,932 by trust here, #7,508 by citations
- Artificial Intelligence — 14 citations into it; #14,796 of 35,345 by trust here, #25,809 by citations
- Genetics — 5 citations into it
- Probabilistic & Bayesian Methods — 5 citations into it
Whom S. Aeschbacher trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Robert E. Schapire
- Yoav Freund
- MARK A. Beaumont
- David Ruppert
- Michael G. B. Blum
- Paul Marjoram
- Jerome H. Friedman
- Paul Joyce
- Peter Buhlmann
- Paul Fearnhead
Who trusts S. Aeschbacher overall
- Michael U Gutmann
- Traiko Dinev
- Daniel R. Schrider
- Mijung Park
- Pierre Pudlo
- Andrew D. Kern
- Ritabrata Dutta
- Wittawat Jitkrittum
- Jukka Corander
- Jean-Michel Marin
Papers from S. Aeschbacher's perspective
Ordered by how strongly the researchers S. Aeschbacher trusts endorse each paper. Same corpus, a different order for every researcher.
- Markov chain Monte Carlo without likelihoods
- The Strength of Weak Learnability
- Special Invited Paper-Additive logistic regression: A statistical view of boosting
- Approximate Bayesian computation in population genetics.
- Greedy function approximation: A gradient boosting machine.
- Non-linear regression models for Approximate Bayesian Computation (Statistics and computing)
- Monte Carlo Methods of Inference for Implicit Statistical Models
- Approximate Bayesian Computation in Evolution and Ecology
- Efficient Approximate Bayesian Computation Coupled With Markov Chain Monte Carlo Without Likelihood (Genetics)
- Approximate Bayesian Computation (ABC) in practice. (Trends in Ecology & Evolution)
Their papers
- A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation (2012) — 117 citations