"... In PAGE 15: ...able 17: Setup Accessibility......................................................................................................123 Table18 : Recommended Log Factors .... ..."

"... In PAGE 7: ...4. Execution time and area Table5 shows estimates of the execution time and area of VHR division and of the version with boosting, using the models presented in [12]. As can be seen from the table, for 54 bits the boosting technique is only effective for log2R = 18, in which case the area ratio (VHR+boost)/(VHR) is 0.... In PAGE 8: ... On the other hand, for 114 bits both very- high radix implementations produce speedups of up to 6; the reduction in area of the implementations of the boosting algorithm being of 25% (for C=4) and of 35% (for C=8), with respect to the standard VHR implementation. More- over, from Table5 b we observe that for 114 bits using an area of about 3000Afa, we can design either a VHR radix- 217 unit with total delay of 100tfa or a VHR+boost radix- 219 unit (with C = 8) with total delay 10% smaller. 7.... ..."

"... In PAGE 11: ... 4.1 Infection Example Table6 shows the logarithm of the Bayes factors for M1 and M2, the xed and random e ect models respectively estimated using Gibbs sampling and approximation methods. For this example Laplace approximation methods could not be used because of the large number of parameters involved.... In PAGE 11: ... The prior distribution for the between-study variance parameter, 2, was chosen to be an Inverted 2 with one degree of freedom, corresponding to an IG(0:5; 2), which represents a relatively di use prior distribution. gt;From Table6 we can conclude that a random e ect model is considered to be the most appropriate. The approximate methods yield very similar results to those obtained using Gibbs sampling, and both are in agreement with the Classical 2 for heterogeneity test presented in Section 2.... In PAGE 12: ...Dentifrice Example Using again an inverted 2 with one degree of freedom prior distribution for 2 Table6 shows the estimated log Bayes factors using approximate, Gibbs sampling and Laplace methods. The estimated logarithms of the marginals are very similar using the three methods, and all three yield log bayes factors which provide evidence in favour of a xed e ect model.... In PAGE 12: ...4.1 Fixed versus Random Using again an inverted 2 with one degree of freedom prior distribution for 2 Table6 shows the estimated log Bayes factors using approximate and Gibbs sampling. For this particular example we see an overwhelming level of evidence in favour of the random e ect model.... ..."

"... In PAGE 6: ...ion of the B.I.F. Board of Trustees (approvalH110050 or rejectionH110051) on the two factors that resulted from the factor analysis in TABLE3 (n H11005 414). Parameter df Estimate Wald-H92732 R2 (new-scaled) Total 2 23.... ..."