Bandingkan kaedah

	Metropolis-Hastings dengan Ralat Pengukuran	Hamiltonian Monte Carlo dengan Ralat Pengukuran
Bidang	Bayesian	Bayesian
Keluarga	Bayesian methods	Bayesian methods
Tahun asal	1953 (base algorithm); 1990s (measurement-error application)	2006-2011
Pengasas	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
Jenis	MCMC sampling algorithm	Bayesian sampling algorithm for latent-variable models
Sumber perintis	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
Alias	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
Berkaitan	4	6
Ringkasan	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.