Linganisha mbinu

	Regression ya Kiasi ya Bayesian	Regression Imara ya Bayesian
Nyanja	Takwimu	Takwimu
Familia	Regression model	Regression model
Mwaka wa asili	2001–2011	1993
Mwanzilishi	Kozumi & Kobayashi; building on Yu & Moyeed (2001)	Geweke (1993); Gelman et al. (2013)
Aina	Bayesian semiparametric regression	Bayesian regression with heavy-tailed errors
Chanzo asilia	Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation, 81(11), 1565–1578.	Geweke, J. (1993). Bayesian treatment of the independent Student-t linear model. Journal of Applied Econometrics, 8(S1), S19–S40.
Majina mbadala	BQR, Bayesian quantile regression model, asymmetric Laplace Bayesian regression, posterior quantile regression	Bayesian heavy-tailed regression, Bayesian Student-t regression, robust Bayesian linear model, BRR
Zinazohusiana	6	6
Muhtasari	Bayesian Quantile Regression estimates the full posterior distribution of regression coefficients at any chosen quantile of the outcome. By combining the asymmetric Laplace likelihood with prior distributions over the coefficients, it delivers uncertainty-quantified estimates of conditional quantiles — such as the median, the 10th, or the 90th percentile — without assuming Gaussian errors.	Bayesian Robust Regression replaces the Gaussian error assumption of ordinary linear regression with a heavy-tailed distribution — most commonly the Student-t — and estimates all parameters in a Bayesian framework. The heavier tails give outliers less influence on the fitted line, yielding stable coefficient estimates and honest uncertainty intervals even when the data contain unusual observations.