ABC–CDE: Toward Approximate Bayesian Computation With Complex High-Dimensional Data and Limited Simulations

DOI10.1080/10618600.2018.1546594OpenAlexW2964291165WikidataQ128855457 ScholiaQ128855457MaRDI QIDQ3391259

Rafael Izbicki, Ann B. Lee, Taylor Pospisil

Publication date: 28 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2018.1546594

zbMATH Keywords

approximate Bayesian computationlikelihood-free inferencenonparametric methodsconditional density estimation

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

Fast Bayesian inversion for high dimensional inverse problems, Marginally Calibrated Deep Distributional Regression, The frontier of simulation-based inference, A comparative study on high-dimensional bayesian regression with binary predictors, Detecting conflicting summary statistics in likelihood-free inference

Uses Software

• R
• ElemStatLearn
• abc
• XGBoost
• abcrf
• RFCDE

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