### Table 2: Parameters of the Discrete-Time Model

2003

### Table 1 Semantic properties of some well-known models for associations

"... In PAGE 9: ... Hiring and firing employees correspond to CRUD operations. Summary of comparison The main features of these models are summarized in the Table1 . The ODMG model is a simple one, but one with a strong mathematical basis since it is based on the concept of a mathematical relation.... ..."

### Table 1 Semantic properties of some well-known models for associations

"... In PAGE 9: ... Hiring and firing employees correspond to CRUD operations. Summary of comparison The main features of these models are summarized in the Table1 . The ODMG model is a simple one, but one with a strong mathematical basis since it is based on the concept of a mathematical relation.... ..."

### Table 3: Discrete-time Erlang model, s = 200.

1999

### Table 1 Summary of discrete-time extended Kalman lter (EKF).

1996

"... In PAGE 3: ... This model can be expressed in augmented state-space form as: (k + 1) = F( (k); u(k)) + w(k) (17) where = [xT ; T ]T is the augmented state vec- tor, w = [wT 1 ; T ]T and: F( (k); u(k)) = f(x(k); u(k); (k)) (k) (18) Furthermore, it is assumed that the measurement equation can be written: z(k) = H( (k)) + v(k) (19) where z 2 IRm and m is the number of sensors. The discrete-time extended Kalman lter algo- rithm in Table1 can then be applied to estimate = [xT ; T ]T in (17) by means of the measure- ment (19). For details on the implementation is- sues see Gelb et al.... ..."

Cited by 3

### Table 1 Summary of discrete-time extended Kalman lter (EKF).

1996

"... In PAGE 3: ... This model can be expressed in augmented state-space form as: (k + 1) = F( (k); u(k)) + w(k) (17) where = [xT ; T ]T is the augmented state vec- tor, w = [wT 1 ; T ]T and: F( (k); u(k)) = f(x(k); u(k); (k)) (k) (18) Furthermore, it is assumed that the measurement equation can be written: z(k) = H( (k)) + v(k) (19) where z 2 IRm and m is the number of sen- sors. The discrete-time extended Kalman lter algorithm in Table1 can then be applied to esti- mate = [xT ; T ]T in (17) by means of the mea- surement (19). For details on the implementation issues, see (Gelb et al.... ..."

Cited by 3

### Table 1. Discrete-time simulation results for quadratic friction case.

"... In PAGE 7: ...0. The results in Table1 show that although the pure discrete wheel model simulation executes at a faster rate, its accuracy leaves much to be desired. Figure 7.... ..."

### Table 1. Discrete-time simulation results for quadratic friction case.

"... In PAGE 7: ...0. The results in Table1 show that although the pure discrete wheel model simulation executes at a faster rate, its accuracy leaves much to be desired. Figure 7.... ..."

### Table 2. A well-known medical dataset used as a test dataset for binary discrimination with kernel models

"... In PAGE 15: ... 5.2 Discrete Density Estimation Table2 represents a well-known medical binary data set described in Anderson et al. [26] and used throughout the statistical literature to test non-parametric statistical mod- els [27, 28, 29].... In PAGE 15: ... The aim is then to build a model for the data showing which combination of symptoms are most likely to indicate the presence of the disease in pa- tients. For the data in Table2 , we obtained the Bernoulli mix- ture model given in Table 3. Note that we can read off the most weighty pattern in Table 1 from the mixture model in Table 3.... In PAGE 15: ... Note that we can read off the most weighty pattern in Table 1 from the mixture model in Table 3. More specifically, observations 20, 32 and 36 are representatives of the most predominant binary pattern in Table2 . Patients exhibiting these patterns of symptoms would therefore most likely be considered stricken with the disease.... ..."