BLUM Michael

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  • Laboratoire TIMC-IMAG, Univ. Grenoble Alpes, Grenoble, France
  • Adaptation, Bioinformatics & Computational Biology, Human Evolution, Population Genetics / Genomics
  • recommender

1 recommendation

2017-11-17
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PREPRINT
ABC random forests for Bayesian parameter inference
Louis Raynal, Jean-Michel Marin, Pierre Pudlo, Mathieu Ribatet, Christian P. Robert, Arnaud Estoup
https://arxiv.org/pdf/1605.05537

Recommended by Michael Blum based on reviews by Michael Blum and Dennis Prangle
Machine learning methods are useful for Approximate Bayesian Computation in evolution and ecology

It is my pleasure to recommend the paper by Raynal et al. [1] about using random forest for parameter inference. There are two reviews about the paper, one review written by Dennis Prangle and another review written by myself. Both reviews were positive and included comments that have been addressed in the current version of the preprint.

The paper nicely shows that modern machine learning approaches are useful for Approximate Bayesian Computation (ABC) and more generally for simulation-dri...

More

1 review

2017-11-17
article picture
PREPRINT
ABC random forests for Bayesian parameter inference
Louis Raynal, Jean-Michel Marin, Pierre Pudlo, Mathieu Ribatet, Christian P. Robert, Arnaud Estoup
https://arxiv.org/pdf/1605.05537

Recommended by Michael Blum based on reviews by Michael Blum and Dennis Prangle
Machine learning methods are useful for Approximate Bayesian Computation in evolution and ecology

It is my pleasure to recommend the paper by Raynal et al. [1] about using random forest for parameter inference. There are two reviews about the paper, one review written by Dennis Prangle and another review written by myself. Both reviews were positive and included comments that have been addressed in the current version of the preprint.

The paper nicely shows that modern machine learning approaches are useful for Approximate Bayesian Computation (ABC) and more generally for simulation-dri...

More