### Table 1: Posterior distribution of the number of components k.

2000

"... In PAGE 9: ...Some results For each set of data our results are based on 1,000,000 sweeps of the MCMC algorithm after a burn-in of 100,000 sweeps. Table1 shows the posterior distribution of the number of components k.... In PAGE 9: ...lose to the MLEs 0.037, 0.069, 0.0046 and 0.0092, respectively, found in Ryden et al. (1998). For the wind data, Table1 suggests k = 3. Francq and Roussignol (1997) proposed k = 2 but, as noted above, they used a somewhat di erent model.... In PAGE 9: ... For k = 5 the curve overlaps, however. For the magnetic dataset, Table1 again suggests k = 3 components. Francq and Roussignol (1997) proposed k = 2 with their MLE yielding a loglikelihood of about ?6101.... In PAGE 14: ... The re- sults are quite di erent from what is obtained modelling with HMMs. Figure 8 shows the posterior distributions for k, which are much more spread out than in Table1 . For the S amp;P 500 and wind data, there is even mass at k = 30, which was our kmax.... ..."

Cited by 28

### Table 3: Simulated posterior distribution of for Yangry and Yhappy

"... In PAGE 9: ...1 for each of the parameters j (j = 1; : : : ; 28) and , another 2000 runs in each sequence were executed, ending up with L = 10000 posterior draws for each criterion. Table3 gives the marginal distribution of the -parameters (or, equiva- lently, the conjunctive combinations) for anger and happiness, respectively. The results of the Bayesian analysis show that the rule found by the deter- ministic branch-and-bound algorithm may be one of several \best quot; solutions:... ..."

### Table 2: Posterior Distribution with Independent Case

### Table 2: Posterior Distribution with Independent Case

### 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 2 Values of the posterior distribution (13) with respect to survival times

in A new Bayes

2006

"... In PAGE 7: ...1. Breast cancer data We compute the value of the posterior distribution (13) for each survival time and these values are given in Table2 and in Fig. 1.... ..."

### Table 2: Selected quantiles of model parameters apos; marginal posterior distributions

1997

"... In PAGE 11: ... The number of initial #5Cwarm-up quot; runs were chosen to be 500,000, and one set of samples of parameters were then taken in every 100 it- erations to avoid serial correlation. The distributions of the six parameters of the piecewise linear model are shown in Figures 3a to 3f, and Table2 shows selected quantiles of these distributions. The mode and the mean of the marginal posterior distribution of #0C 1 are very close to 0, which further elaborated the mechanism hypothesized in the conceptual model.... ..."

Cited by 5

### Table 10 Posterior distribution of treatment effect and its variation across schools

2007

"... In PAGE 33: ... Table10 summarizes the marginal posterior distributions of parameters of interest. Each distribution conveys the posterior probability that a parameter of interest takes on various values.... In PAGE 33: ... Similarly, the 95% interval in the last column can be viewed as a Bayesian analogue of 95% confidence intervals in the frequentist framework, though of course interpretations differ. In Table10 , we see that the average treatment effect on a logit scale is 0.871 with a 95% interval of (0.... In PAGE 34: ... In connection with this, note that there is a substantial negative correlation between u0j and u1j (Corr(u0j, u1j)=-0.583, Table10 ). This correlation indicates that the effect of EAOP is larger in schools where non-EAOP students have a relatively small probability of being A-G eligible.... ..."