Table II.

Bayesian and maximum-likelihood estimates of the parameters of Model A. The different parameters are listed in the first column. The next five columns show the mean, the mode, the median, and the lower and upper bounds of the central 95% interval of the posterior probability distribution (determined by Bayes theorem and computed using a Markov-Chain Monte Carlo technique) for each parameter. The last three columns show the mode of the likelihood function (i.e., the maximum-likelihood estimate), and the lower and upper bounds of the 95% confidence interval estimated based on the likelihood function, assuming normality. Note that, under the normality assumption, the mean and median of the likelihood function coincide with the mode.

	Bayes mean	Bayes mode	Bayes median	Bayes 2.5%	Bayes 97.5%	ML mode	ML 2.5%	ML 97.5%
γf	0.82	0.83	0.82	0.76	0.88	0.82	0.76	0.88
γd	−0.42	−0.42	−0.42	−0.57	−0.28	−0.48	−0.63	−0.33
γs	−1.09	−1.09	−1.09	−1.46	−0.75	−1.16	−1.53	−0.78
βf	0.38	0.37	0.37	0.33	0.44	0.38	0.32	0.43
βd	0.42	0.40	0.40	0.24	0.71	0.33	0.16	0.50
βs	0.37	0.37	0.37	0.27	0.50	0.36	0.25	0.46
α	−0.38	−0.36	−0.37	−0.37	−0.08	−0.29	−0.47	−0.12