machine learning methods are useful for ABC [or my first PCI Evol Biol!]

While I am still working on setting a PCI [peer community in] Comput Stats, having secure sponsorship of some societies (ASA, KSS, RSS, SFdS, and hopefully ISBA), my coauthors Jean-Michel Marin and Louis Raynal submitted our paper ABC random forests for Bayesian parameter inference to PCI Evol Biol. And after a few months of review, including a revision accounting for the reviewers’ requests, our paper stood the test and the recommendation by Michael Blum and Dennis Prangle got published there. Great news, and hopefully helpful for our submission within the coming days!

7 Responses to “machine learning methods are useful for ABC [or my first PCI Evol Biol!]”

  1. Thanks for posting this, I enjoyed reading the new additions (and didn’t spot where to find the response letter previously!)

  2. Christian Matthes Says:

    Could you elaborate on what the exact purpose of a PCI is? Is it basically a device to get peer review before submission to an ‘actual’ journal?

    • The appeal of a PCI system is to create a community of active volunteers that are willing to spend some time together to assess preprints they deem of interest to bring a add of public validation to the availability of the preprint. Whether or not this assessment is later used by the author(s) of the preprint when submitting to a journal or by reviewers of the said journal when refereeing the paper is a matter of choice by these actors. One may imagine authors that are happy enough with obtaining a PCI validation, without engaging into a further journal review process. In my opinion, a signed and open access assessment is the major thing to distinguish between preprints. Note also that published papers can also be reviewed by the community, if it deems the paper of sufficient interest.

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