Linganisha mbinu

	Sequential Monte Carlo yenye Hitilafu ya Upimaji	Utoaji wa Kibayesia kwa Kosa la Kipimo
Nyanja	Mbinu za Bayes	Mbinu za Bayes
Familia	Bayesian methods	Bayesian methods
Mwaka wa asili	1993–2001	1993
Mwanzilishi	Gordon, Salmond & Smith (1993); extended by Doucet, de Freitas & Gordon (2001)	Richardson & Gilks (Bayesian formulation); Carroll et al. (comprehensive framework)
Aina	Sequential Bayesian filtering	Bayesian errors-in-variables model
Chanzo asilia	Doucet, A., de Freitas, N., & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer New York. ISBN: 978-0-387-95146-1	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
Majina mbadala	SMC with measurement error, particle filter with noisy observations, SMC state-space measurement error, sequential particle filtering with observation noise	Bayesian errors-in-variables model, Bayesian EIV model, Bayesian measurement error model, Bayesian misclassification model
Zinazohusiana	6	5
Muhtasari	Sequential Monte Carlo (SMC) with measurement error is a particle-based Bayesian filtering method for tracking hidden states in dynamical systems when observations are corrupted by noise. It propagates a weighted cloud of particles through time, updating weights at each step to reflect how well each particle explains the noisy measurement, and produces a full posterior distribution over the latent state at every time point.	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.