Table 2-2. The results of the Gibbs sampler algorithm. The likelihood estimates for thesame data
set were obtained using the BFGS algorithm for optimization. Prior shape = 10
scale=l. Number of iterations in the Gibbs sampler were 60,000 and the first 20,000
iterations were excluded in calculating the summary statistics.
Posterior Posterior
standard standard Likelihood Likelihood
Posterior deviation Posterior deviation estimate of estimate of
R T mean of r of r mean of A of A r A
50 3 0.314 0.096 10.035 2.963 0.929 25.511
50 10 0.348 0.087 11.045 2.927 0.564 27.647
25 3 0.264 0.086 9.761 2.997 0.810 14.563
25 10 0.362 0.098 10.583 2.900 0.694 13.897
Table 2-3. The results of the Gibbs sampler algorithm. The likelihood estimates for the same data
set were obtained using the BFGS algorithm for optimization. Prior shape = 1
scale=10. Number of iterations in the Gibbs sampler were 60, 000 and the first 20,000
iterations were excluded in calculating the summary statistics.
Posterior Posterior
standard standard Likelihood
Posterior deviation Posterior deviation Likelihood estimate of
R T mean ofr of r mean of A of A estimate ofr A
50 3 0.319 0.150 11.347 5.843 0.929 25.511
50 10 0.271 0.118 16.654 8.606 0.564 27.647
25 3 0.285 0.156 11.201 7.150 0.810 14.563
25 10 0.349 0.169 12.959 6.804 0.694 13.897