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Robust Approximate Bayesian Computation/Evidence
Method evidence record

Robust Approximate Bayesian Computation

Robust ABC extends standard Approximate Bayesian Computation to handle outliers, model misspecification, and sensitivity to summary statistic choice. By replacing conventional distance measures with robust alternatives — such as composite scores, trimmed statistics, or synthetic likelihoods — it protects posterior inference from being distorted by atypical observations or an imperfect simulator.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Robust Approximate Bayesian Computation
Taxonomic method record · bayesian / bayesian
  • Ruli, E., Sartori, N. & Ventura, L. (2016). Approximate Bayesian computation with composite score functions. Statistics and Computing, 26(3), 679–692. · DOI 10.1007/s11222-015-9551-z
  • Frazier, D. T., Drovandi, C. & Nott, D. J. (2020). Robust Approximate Bayesian Inference with Synthetic Likelihood. Journal of Computational and Graphical Statistics, 30(4), 958–976. · DOI 10.1080/10618600.2021.1875839
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Related methods

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See alsoApproximate Bayesian Computationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketBayesian Inference with Measurement Errormachine-suggested · Relational suggestion, not evidence.Same method familyParticle Filtermachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust Bayesian Inferencemachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust Variational Inferencemachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSequential Monte Carlomachine-suggested · Relational suggestion, not evidence.

Evidence status

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Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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