3 citations · 3 across the 3 of their papers we have counts for
3 papers
Cost-based feature selection for network model choice
Louis Raynal, Till Hoffmann, Jukka-Pekka Onnela
Selecting a small set of informative features from a large number of possibly noisy candidates is a challenging problem with many applications in machine learning and approximate B…
Scalable Approximate Bayesian Computation for Growing Network Models via Extrapolated and Sampled Summaries
Louis Raynal, Sixing Chen, Antonietta Mira +1
Approximate Bayesian computation (ABC) is a simulation-based likelihood-free method applicable to both model selection and parameter estimation. ABC parameter estimation requires t…
ABC random forests for Bayesian parameter inference
Louis Raynal, Jean-Michel Marin, Pierre Pudlo +3
This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100036). Approximate Bayesian computation (ABC) h…