R package abcrf: ABC random forests for Bayesian parameter inference. Approximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian inference for models associated with intractable likelihood functions. Most ABC implementations require the preliminary selection of a vector of informative statistics summarizing raw data. Furthermore, in almost all existing implementations, the tolerance level that separates acceptance from rejection of simulated parameter values needs to be calibrated. We propose to conduct likelihood-free Bayesian inferences about parameters with no prior selection of the relevant components of the summary statistics and bypassing the derivation of the associated tolerance level. The approach relies on the random forest methodology of Breiman (2001) applied in a (non parametric) regression setting. We advocate the derivation of a new random forest for each component of the parameter vector of interest. When compared with earlier ABC solutions, this method offers significant gains in terms of robustness to the choice of the summary statistics, does not depend on any type of tolerance level, and is a good trade-off in term of quality of point estimator precision and credible interval estimations for a given computing time. We illustrate the performance of our methodological proposal and compare it with earlier ABC methods on a Normal toy example and a population genetics example dealing with human population evolution. All methods designed here have been incorporated in the R package abcrf (version 1.6) available on CRAN.

References in zbMATH (referenced in 13 articles )

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  1. Freund, Fabian; Siri-J├ęgousse, Arno: The impact of genetic diversity statistics on model selection between coalescents (2021)
  2. Hendricks, Jessie; Neumann, Cedric; Saunders, Christopher P.: Quantification of the weight of fingerprint evidence using a ROC-based approximate Bayesian computation algorithm for model selection (2021)
  3. Cope, Robert C.; Ross, Joshua V.: Identification of the relative timing of infectiousness and symptom onset for outbreak control (2020)
  4. McKinley, Trevelyan J.; Neal, Peter; Spencer, Simon E. F.; Conlan, Andrew J. K.; Tiley, Laurence: Efficient Bayesian model choice for partially observed processes: with application to an experimental transmission study of an infectious disease (2020)
  5. Moores, Matthew; Nicholls, Geoff; Pettitt, Anthony; Mengersen, Kerrie: Scalable Bayesian inference for the inverse temperature of a hidden Potts model (2020)
  6. Rodrigues, G. S.; Nott, David J.; Sisson, S. A.: Likelihood-free approximate Gibbs sampling (2020)
  7. Xu, Jason; Koelle, Samson; Guttorp, Peter; Wu, Chuanfeng; Dunbar, Cynthia; Abkowitz, Janis L.; Minin, Vladimir N.: Statistical inference for partially observed branching processes with application to cell lineage tracking of \textitinvivo hematopoiesis (2019)
  8. Dutta, Ritabrata; Mira, Antonietta; Onnela, Jukka-Pekka: Bayesian inference of spreading processes on networks (2018)
  9. Gutmann, Michael U.; Dutta, Ritabrata; Kaski, Samuel; Corander, Jukka: Likelihood-free inference via classification (2018)
  10. Karabatsos, George; Leisen, Fabrizio: An approximate likelihood perspective on ABC methods (2018)
  11. Lee, Xing Ju; Hainy, Markus; McKeone, James P.; Drovandi, Christopher C.; Pettitt, Anthony N.: ABC model selection for spatial extremes models applied to south Australian maximum temperature data (2018)
  12. Rodrigues, G. S.; Prangle, D.; Sisson, S. A.: Recalibration: a post-processing method for approximate Bayesian computation (2018)
  13. Louis Raynal, Jean-Michel Marin, Pierre Pudlo, Mathieu Ribatet, Christian P. Robert, Arnaud Estoup: ABC random forests for Bayesian parameter inference (2016) arXiv