23 citations · 118 across the 21 of their papers we have counts for
6 papers · 1 filter
Reconciling Binary Replicates: Beyond the Average
Manuela Royer-Carenzi, Hadrien Lorenzo, Pierre Pudlo
Binary observations are often repeated to improve data quality, creating technical replicates. Several scoring methods are commonly used to infer the actual individual state and ob…
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…
Bayesian functional linear regression with sparse step functions
Paul-Marie Grollemund, Christophe Abraham, Meïli Baragatti +1
The functional linear regression model is a common tool to determine the relationship between a scalar outcome and a functional predictor seen as a function of time. This paper foc…
Likelihood-free Model Choice
Jean-Michel Marin, Pierre Pudlo, Arnaud Estoup +1
This document is an invited chapter covering the specificities of ABC model choice, intended for the incoming Handbook of ABC by Sisson, Fan, and Beaumont (2017). Beyond exposing t…
Some discussions of D. Fearnhead and D. Prangle's Read Paper "Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation"
Christophe Andrieu, Simon Barthelme, Nicolas Chopin +11
This report is a collection of comments on the Read Paper of Fearnhead and Prangle (2011), to appear in the Journal of the Royal Statistical Society Series B, along with a reply fr…
Monte Carlo algorithms for model assessment via conflicting summaries
Oliver Ratmann, Pierre Pudlo, Sylvia Richardson +1
The development of statistical methods and numerical algorithms for model choice is vital to many real-world applications. In practice, the ABC approach can be instrumental for seq…