ABC random forests for Bayesian parameter inference | Zendy

Louis Raynal | Zendy; JeanMichel Marin | Zendy; Pierre Pudlo | Zendy; Mathieu Ribatet | Zendy; Christian P. Robert | Zendy; Arnaud Estoup | Zendy

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ABC random forests for Bayesian parameter inference

Author(s) -

Louis Raynal,

JeanMichel Marin,

Pierre Pudlo,

Mathieu Ribatet,

Christian P. Robert,

Arnaud Estoup

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty867

Subject(s) - approximate bayesian computation , computer science , bayesian probability , robustness (evolution) , inference , parametric statistics , random forest , population , model selection , bayesian inference , implementation , point estimation , estimator , machine learning , data mining , statistics , artificial intelligence , mathematics , biochemistry , chemistry , demography , sociology , gene , programming language

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.

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