Giusi Moffa
Artificial Intelligence · 2014–2018
A researcher in the Science corpus: 3 papers; trusts 44 researchers and is trusted by 23 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 30 citations into it; #7,566 of 35,345 by trust here, #19,873 by citations
- Probabilistic & Bayesian Methods — 25 citations into it; #537 of 1,525 by trust here, #945 by citations
- Statistics and Probability — 17 citations into it; #4,858 of 12,932 by trust here, #6,853 by citations
- Causal Inference & Discovery — 11 citations into it
- Molecular Biology — 8 citations into it
Whom Giusi Moffa trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Nir Friedman
- Dan Geiger
- David E. Heckerman
- Markus Kalisch
- Marco Scutari
- David Madigan
- Diego Colombo
- Jack Kuipers
- Peter Buhlmann
- Leo Breiman
Who trusts Giusi Moffa overall
- Jack Kuipers
- Jussi Viinikka
- Yashas Annadani
- Tristan Deleu
- Antti Hyttinen
- Ricardo M. A. Silva
- Polina Suter
- Jonas Rothfuss
- Gonçalo Faria
- Johan Pensar
Papers from Giusi Moffa's perspective
Ordered by how strongly the researchers Giusi Moffa trusts endorse each paper. Same corpus, a different order for every researcher.
- Equivalence and Synthesis of Causal Models
- Optimal Structure Identification With Greedy Search
- Learning Equivalence Classes of Bayesian Network Structures
- Causality : Models , Reasoning , and Inference
- Bayesian Graphical Models for Discrete Data
- Causation, Prediction, and Search
- Approximating discrete probability distributions with dependence trees
- The max-min hill-climbing Bayesian network structure learning algorithm
- A characterization of Markov equivalence classes for acyclic digraphs
- Learning Bayesian networks: The combination of knowledge and statistical data
Their papers
- Partition MCMC for Inference on Acyclic Digraphs (2015) — 110 citations
- Addendum on the scoring of Gaussian directed acyclic graphical models (2014) — 84 citations
- Efficient Sampling and Structure Learning of Bayesian Networks (2018) — 86 citations