Estimating Tree Height-Diameter Models with the Bayesian Method

Table 3

Parameter estimates of six models using classical and Bayesian methods with uninformative priors using data1.

		Classical method					Bayesian method
Model		Parameter estimate		95% interval		RMSE	Parameter estimate		95% interval		DIC
		Mean	Std. error	Lower	Higher		Mean	Std.	Lower	Higher
Chapman-Richards	a	22.95	2.472	18.10	27.81	1.1554	21.15	0.988	19.56	23.96	1398.34
	b	0.05	0.016	0.02	0.08		0.06	0.009	0.04	0.08
	c	0.87	0.126	0.62	1.12		1.01	0.082	0.83	1.18
Weibull	a	23.59	3.315	17.07	30.10	1.1551	26.58	4.619	20.86	38.67	1368.38
	b	0.07	0.006	0.06	0.08		0.07	0.005	0.06	0.08
	c	0.90	0.100	0.71	1.10		0.85	0.080	0.71	0.99
Logistic	a	19.12	0.661	17.82	20.42	1.1644	19.25	0.797	17.99	21.02	1406.18
	b	3.24	0.233	2.78	3.70		3.27	0.226	2.88	3.73
	c	0.13	0.012	0.11	0.15		0.13	0.012	0.11	0.15
Gompertz	a	28.53	2.322	23.97	33.10	1.1556	26.58	0.721	25.23	27.96	1399.03
	b	−15.48	3.258	−21.88	−9.08		−12.74	0.875	−14.38	−11.07
	c	4.92	2.053	0.89	8.96		3.13	0.604	1.87	4.22
Bertalanffy	a	16.58	0.164	16.26	16.91	1.2421	16.59	0.172	16.31	16.87	1462.49
	b	0.18	0.003	0.17	0.19		0.18	0.003	0.18	0.19
Power law	a	2.78	0.122	2.54	3.02	1.1658	2.80	0.123	2.57	3.04	1406.01
	b	0.57	0.016	0.54	0.60		0.57	0.016	0.54	0.59