Hierarchical Bayesian Spatio-Temporal Modeling for PM₁₀ Prediction

Table 3

Posterior estimates and 95% credible interval for the parameters of the AR, GP, and GPP models fitted for PM₁₀ concentration levels. SD stands for standard deviation.

Models	Parameters	Mean	Median	SD	95% credible interval
AR		1.3521	1.3519	0.0206	(1.3115, 1.3934)
	Temp	0.0001	0.0001	0.0004	(-0.0007, 0.0009)
	RH	0.0000	0.0000	0.0002	(-0.0004, 0.0003)
		0.0160	0.0145	0.0267	(-0.0328, 0.0734)
		0.0209	0.0208	0.0007	(0.0199, 0.0226)
		0.0124	0.0123	0.0009	(0.0107, 0.0144)
		0.0168	0.0168	0.0000	(0.0168, 0.0168)
GP		12.1303	12.4378	1.1060	(9.7744, 13.5797)
	Temp	-0.0173	-0.0267	0.0334	(-0.0590, 0.0538)
	RH	-0.0153	-0.0163	0.0041	(-0.0209, -0.0062)
		0.0052	0.0052	0.0001	(0.0050, 0.0054)
		15.2262	12.8393	7.9727	(6.6616, 35.1414)
		0.0032	0.0027	0.0018	(0.0011, 0.0069)
GPP		0.4792	0.4784	0.0692	(0.3425, 0.6131)
	Temp	-0.0028	-0.0028	0.0013	(-0.0053, -0.0003)
	RH	-0.0013	-0.0013	0.0003	(-0.0018, -0.0007)
		0.9704	0.9704	0.0025	(0.9654, 0.9754)
		0.0052	0.0052	0.0001	(0.0050, 0.0054)
		1.7450	1.7453	0.0256	(1.6955, 1.7954)
		0.0010	0.0010	0.0000	(0.0010, 0.0010)