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Approximate Bayesian Computation with Measurement Error/Evidence
Method evidence record

Approximate Bayesian Computation with Measurement Error

Approximate Bayesian Computation with measurement error (ABC-ME) extends the standard ABC likelihood-free framework to settings where observed data are themselves noisy or imprecisely recorded. By explicitly incorporating a measurement-error kernel into the acceptance step, ABC-ME targets the correct posterior over model parameters even when the true data-generating process cannot be directly observed.

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

Approximate Bayesian Computation with Measurement Error
Taxonomic method record · bayesian / bayesian
  • Wilkinson, R. D. (2013). Approximate Bayesian computation (ABC) gives exact results under the assumption of model error. Statistical Applications in Genetics and Molecular Biology, 12(2), 129-141. · DOI 10.1515/sagmb-2013-0010
  • Beaumont, M. A. (2010). Approximate Bayesian computation in evolution and ecology. Annual Review of Ecology, Evolution, and Systematics, 41, 379-406. · DOI 10.1146/annurev-ecolsys-102209-144621
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See alsoApproximate Bayesian Computationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketBayesian Inference with Measurement Errormachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMCMC with Measurement Errormachine-suggested · Relational suggestion, not evidence.Same method familyParticle Filtermachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSequential Monte Carlomachine-suggested · Relational suggestion, not evidence.

Evidence status

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Sources

2 recorded citations, copied from the method source record.

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