### Table 3 Posterior distributions of response parameters Model for store incidence Model for ln expenditures

2005

"... In PAGE 17: ... We verified both restrictions, and found that display does not affect store incidence significantly, and feature does not affect spending significantly. E RESULTS Store incidence We present the store incidence results in the left-hand part of Table3 . Except for perceived Produce Quality - the impact of which is not significant - the benefit variables (StoreSurface, Feature and LagExpend) have positive and significant effects on store incidence.... In PAGE 18: ...079), plausibly because consumers want to shop for holiday meals, and the longer opening hours (relative to Christmas) allow them to do so. [Insert Table3 about here] Focusing on the impact of the price war variables, several interesting findings emerge. First, Albert Heijn did not manage to increase its store incidence propensity, as the coefficient of the PW*AlbertHeijn variable is insignificant.... In PAGE 18: ....355), and part of which is temporary (-.121). This empirical finding corroborates the prediction of Busse (2002) and Heil and Helsen (2001) that price wars increase the price sensitivity of consumers. Expenditures The estimates for the log (ln) of expenditures equation are given in right-hand part of Table3 . All the benefit variables have the expected positive and significant effects.... ..."

### Table 1: Posterior results.

"... In PAGE 9: ...1 Posterior distribution of the model parameters The posterior densities of the parameters are graphed in Figure 3. Table1 lists some summary statistics of the posterior distributions of the model parameters, namely means, standard deviations and medians. The most striking difference between the DEM/USD data and the FF/DEM data is the variance of the trend component.... ..."

### Table 1: The OSI protocol layers. [Tan88]

"... In PAGE 6: ... Each layer expects certain services from the layer below and is expected to provide certain services to the layer above. The layers in the OSI model are presented in Table1 . The IP protocol itself lies within the network layer as it is used to support internetworking.... ..."

### Table 2: Posterior Model Probabilities

1999

"... In PAGE 16: ...3 Model Selection Results We calculate posterior probabilities for the various models using equal prior probabilities for each model. 14 The results are presented in Table2 . Since none of the 3TAR models receive probability greater than 0.... In PAGE 16: ... If the reader wishes to choose di#23230Berent prior model probabilities, he#23232Fshe can simply reweight the numbers in the tables. For instance, if one wishes to double the prior weightattached to each of the linear models, then the model probabilities in the #23235CLinear quot; rowof Table2 can be doubled and the other rows downweighted appropriately.... In PAGE 19: ... We concentrate on GI averaged across all models. 15 The weights in this average are given by the posterior model probabilities given in Table2 . Appendix C presents results for the preferred model #2323282TAR3HET with p = 5#232329 and the most popular linear model #232328p = 5#232329 separately.... ..."

Cited by 7

### Table 2: Posterior Model Probabilities

"... In PAGE 16: ...3 Model Selection Results We calculate posterior probabilities for the various models using equal prior probabilities for each model.14 The results are presented in Table2 . Since none of the 3TAR models receive probability greater than 0.... In PAGE 16: ... If the reader wishes to choose di#230Berent prior model probabilities, he#232Fshe can simply reweight the numbers in the tables. For instance, if one wishes to double the prior weightattached to each of the linear models, then the model probabilities in the #235CLinear quot; rowof Table2 can be doubled and the other rows downweighted appropriately.... In PAGE 19: ... We concentrate on GI averaged across all models.15 The weights in this average are given by the posterior model probabilities given in Table2 . Appendix C presents results for the preferred model #23282TAR3HET with p = 5#2329 and the most popular linear model #2328p = 5#2329 separately.... ..."

### Table 7: posterior median and 95 % posterior intervals (PI) for the conjunctive model

"... In PAGE 9: ... Their algorithm could be easily modified to implement the restricted variant of the model that is used in the present paper. Table7 presents the posterior median (point estimate) and the 95% posterior interval of the party parameters for the conjunctive model. The parameters in Table 7 indicate the probability that a certain criterion is necessary for the success of the entire project.... In PAGE 9: ... Table 7 presents the posterior median (point estimate) and the 95% posterior interval of the party parameters for the conjunctive model. The parameters in Table7 indicate the probability that a certain criterion is necessary for the success of the entire project. Before discussing the results in detail, a general remarks should be made: on the basis of the 95% posterior intervals of the estimated probabilities, one can conclude that differences in the perceived necessity of different criteria for the same party are generally larger than the differences in the perceived necessity of a the same criterion for different parties.... ..."

### Table 2. Posterior summaries for the

2002

"... In PAGE 9: ....3. Results In this subsection we discuss the results of the application of the methodology proposed to perform inference about the permeability eld through the analysis of the production and measurement data. Table2 presents posterior summaries for the hyperparameters of the multi-scale model. The estimates of 0, 2 0, 1 and 2 1 are evidence of the higher degree of smoothing/regularization obtained at the intermediate level when compared with the regularization at the coarsest level.... ..."

Cited by 4

### Table 3. Posterior Moments for the Stochastic Volatility Model

2001

"... In PAGE 9: ... 3.2 Empirical Results for the Stochastic Volatility Model Figure 4 and Table3 summarize the posterior simula- tions for the stochastic volatility model. Notice that the results in the table correspond to a re-parameterization of the model that improve upon the convergence speed of the algorithm.... ..."

Cited by 30

### Table 1: Posterior summary of model parameters.

"... In PAGE 3: ... The MCMC algorithm was run from a random start for 20,000 iterations, with the first 10,000 discarded, and then every 20th collected to produce a posterior sample of size 500. Parameter traces are shown in Figure 3; posterior summaries of mean and standard deviation in Table1 ; parameter correlations are in Table 2; and the posterior conductivity distribution in Figure 4. 0 100 200 300 400 500 0.... In PAGE 4: ...Table 1: Posterior summary of model parameters. Notice, from Table1 , that the angular component of the centre is more precisely estimated than the radial component. In contrast, the conductivity and size are less well recovered.... ..."