方法对比
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| 带测量误差的近似贝叶斯计算× | 带有测量误差的贝叶斯推断× | |
|---|---|---|
| 领域 | 贝叶斯 | 贝叶斯 |
| 方法族 | Bayesian methods | Bayesian methods |
| 起源年份≠ | 2013 (measurement-error extension); ABC: 1997-2002 | 1993 |
| 提出者≠ | Wilkinson, R. D. (formal treatment); ABC roots: Tavaré, Diggle, Beaumont et al. (1997-2002) | Richardson & Gilks (Bayesian formulation); Carroll et al. (comprehensive framework) |
| 类型≠ | likelihood-free Bayesian inference | Bayesian errors-in-variables model |
| 开创性文献≠ | 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 ↗ | Carroll, R. J., Ruppert, D., Stefanski, L. A., & Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective (2nd ed.). Chapman & Hall/CRC. ISBN: 978-1584886433 |
| 别名 | ABC with measurement error, ABC-ME, likelihood-free inference with measurement error, simulation-based inference under measurement error | Bayesian errors-in-variables model, Bayesian EIV model, Bayesian measurement error model, Bayesian misclassification model |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | Bayesian inference with measurement error extends the standard Bayesian framework to situations where one or more covariates or outcomes are observed with noise or misclassification. By treating the true unobserved values as latent variables and assigning them priors, the model jointly estimates the true exposure distribution and the structural parameters of interest, propagating all uncertainty through the posterior. |
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