Vertaile menetelmiä
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| Metropolis-Hastings mittausvirheellä× | Hamiltonin Monte Carlo -menetelmä mittausvirheellä× | |
|---|---|---|
| Tieteenala | Bayesilainen tilastotiede | Bayesilainen tilastotiede |
| Menetelmäperhe | Bayesian methods | Bayesian methods |
| Syntyvuosi≠ | 1953 (base algorithm); 1990s (measurement-error application) | 2006-2011 |
| Kehittäjä≠ | Metropolis et al. (1953); measurement-error extension developed in the 1990s Bayesian literature | Neal (2011) for HMC; Carroll et al. (2006) for measurement error framework |
| Tyyppi≠ | MCMC sampling algorithm | Bayesian sampling algorithm for latent-variable models |
| Alkuperäislähde | Carroll, R. J., Ruppert, D., Stefanski, L. A., & Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective (2nd ed.). Chapman and Hall/CRC. ISBN: 978-1584886334 | Carroll, R. J., Ruppert, D., Stefanski, L. A., & Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective (2nd ed.). Chapman and Hall/CRC. ISBN: 978-1584886334 |
| Rinnakkaisnimet | MH with measurement error, Metropolis-Hastings errors-in-variables, MCMC errors-in-variables, Bayesian errors-in-variables MCMC | HMC measurement error model, Bayesian errors-in-variables with HMC, HMC latent variable measurement error, Hamiltonian MCMC with covariate error |
| Liittyvät≠ | 4 | 6 |
| Tiivistelmä≠ | Metropolis-Hastings with measurement error is a Bayesian MCMC approach that jointly estimates model parameters and the true (unobserved) covariate values when predictors or outcomes are recorded with noise. By treating the latent true values as unknown parameters, it propagates measurement uncertainty fully into posterior inference rather than ignoring it or correcting for it post hoc. | Hamiltonian Monte Carlo (HMC) with measurement error is a Bayesian computational strategy for fitting models where one or more covariates are observed with noise. HMC samples jointly from the posterior over model parameters and the unobserved true covariate values, using gradient-based proposals that explore the high-dimensional posterior efficiently and avoid the slow random-walk behaviour of standard Metropolis sampling. |
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