### Table 2. Synthetic Data Case: Model Parameters

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"... In PAGE 6: ... Radar system and environmental parameters are given in Table 1. A typical four-parameter range- independent trilinear profile (Figure 1) is used with the unknown environment parameters and the selected upper and lower limits given in Table2 . The unknown model parameters are the slope and height of the base layer (c1 and h1) and the slope and thickness of the inversion layer (c2 and h2).... ..."

### Table 2 We can use a smoother value of risk if we let the Unknown parameter take real values between -1 and +1. We obtain a continuous model of risk as shown in Figure 1 :

"... In PAGE 5: ...In the Table 1, we give the values associated with the friend moves in the games of J1 for the three strategies : Games\Strategies Risky Average Safe GG 0 0 0 GI 0 1 2 GP 2 2 2 IG 0 -1 -2 II 0 0 0 IP 2 1 0 PG -2 -2 -2 PI 0 -1 -2 PP 0 0 0 Table 1 We give in Table2 the values associated with the moves of some games in the set J2 for the three strategies in a complex game, using Valc : Games\Strategies Risky Average Safe GGGG 0 0 0 GGGI 0 1/2 1 GGII 0 1 2 GIGI 0 0 0 GIII 0 1/2 1 IIII 0 0 0 IIIP 1 1/2 0 IPIP 0 0 0 IIPP 2 1 0 IPPP 1 1/2 0 PPPP 0 0 0 Table 2 We can use a smoother value of risk if we let the Unknown parameter take real values between -1 and +1. We obtain a continuous model of risk as shown in Figure 1 :... ..."

### Table 3. Some Derived Relationships for a General Poisson Process

"... In PAGE 15: ... In addition, unknown model parameters are usually determined using the maximum likelihood principle, least squares, or Bayesian techniques. Again, specific derivations are omitted; however, Table3 provides a summary of some important relationships for these models (Musa, et al., 1987).... ..."

### Table 5. University freshmen: The final friendship model. Estimated parameters of the transition from at most a neutral relationship (unknown, troubled, or neutral relationship) to at least a friendly relationship (friendly relationship, friendship or best friendship) at six transitions in time. Standard errors between parentheses. Sig- nificant parameters (at least twice their standard error) are printed boldface. Parameters that are not shown are smaller than once their standard errors and are fixed at zero, after which the model is estimated again.

"... In PAGE 21: ...56, so also similarity with respect to sex and age are significant factors during the process. Table5 shows the results of the final model, which also includes structural effects. Discussion of Table 5: The structural effects show that especially the effects of reciprocity andtransitivityareofgreatimportance.... ..."

### Table 1: Constrained and unconstrained (treating V ar( quot;n) as unknown) QML estimates of the parameters of the linear SV model, reporting results for the implied inverse Gaussian model. The 90 percent con dence intervals are constructed by a parametric bootstrap using an inverse Gaussian assumption on n. We used 250 replications.

"... In PAGE 16: ...aussian assumption on n. We used 250 replications. For each of these series we used the QML method given in equation (50) to estimate the parameters of the model. These are given in Table1 , together with 90 per cent con dence intervals constructed by a simple parametric bootstrap using an inverse Gaussian assumption on f ng and 250 replications. For ease of later comparison we have mapped the parameters into those for an inverse Gaussian model for n, so that the E( n) = and Var( n) = 3= .... ..."

### Table 1. Inversion Model With Parameter Search Boundsa

"... In PAGE 5: ..., 2003a, Appendix A], see Figure 5. We then search for refractivity parameters at both 0- and 100-km range with the unknown parameters and their search range as given in Table1 . Thus we have 8 unknown parameters for the refractivity profile.... ..."

### Table 1: Most of machine learning, represented in terms of directed graphical models. Nodes on the left represent training data, nodes on the right represent test data, and nodes in the middle (with Greek letters, plus w) are parameters. Shaded nodes are observed, unshaded nodes are unknown. The first four methods are supervised, the rest are unsupervised.

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