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where jt and lt vary over the possible labour force states in our case, E, U and O, with s = 3. In order to proceed in formulating our model, it is crucial to consider the equivalence between latent class and log-linear models with some unobservable variables (Haberman, 1979). An LCM model may also be specified in the log-linear context through the so-called modified LISREL approach proposed by Hagenaars (1990, 1998), which extends Goodman s modified path analysis (Goodman, 1973) to include latent variables. Each conditional probability in Equation (21.1) may be expressed as a function of the log-linear parameters. For example, al1 j1 (1) can be written as:

0.08 Absorbance 0.06 0.04 0.02 0.00 2000 1800 1600 1400 1000 1200 800 Wavenumber (cm 1) (b)

9.2.4.5 Weighted De cit Round Robin (WDRR)

where dist is the Euclidean distance in Rd . In Exercise 8.2 it is proved that (S) < for various relevant classes of parameter sets:

The decision tree in Figure 7.24 was built from a data set of 352 cars, using the continuous variable MPG to split the observations. The average value shown in the diagram is the MPG value for the set of observations. The nodes were not split further if there were less than 30 observations in the terminal node. In Table 7.17, a set of 36 observations not used in building the tree are shown with both an actual and a predicted value. The nal column indicates the node in the tree that was used to calculate the prediction. For example, the AMC Gremlin with a Horsepower of 90 and Weight of 2648 will t into a region de ned by node D in the tree. Node D has an average MPG value of 23.96 and hence this is the predicted MPG value. The table also indicates the actual MPG values for the cars tested. The examples used in this section were simple in order to describe how predictions can be made using decision trees. It is usual to use larger numbers of descriptor variables. Also, building a series of models based on changing the terminating criteria can also be useful in optimizing the decision tree models. The further reading section of chapter 6 provides references to additional methods for optimizing decision trees. The terminal nodes in the decision trees can be described as rules, (as shown in Section 6.3) which can be useful in explaining how a prediction was obtained. In addition, looking at the data that each rule is based on allows us to understand the degree of con dence with which each prediction was made. For example, the

Figure 13.10 A platform server can register at the pro le server to be noti ed when a certain pro le is modi ed. Then, a copy of the pro le is sent to the platform server after each update

Figure A 33: If you need environment entries for the resource factories, use this dialog. At last we come to something new! The dialog shown in Figure A 34 is where you provide Web client specific configuration information. At the top of the window, you provide define any HTML files that are used by the Web server. Welcome files are those which are used by default if you do not provide a file. If you are familiar with standard Web page design, this is the equivalent of the index.html file. So, if a user requested the URL

(2.4)

Unsupported processing. The lack of support in the servlet specification for the pre- and postprocessing of requests and responses prior to the 2.3 release is probably the most typical cause. Because so many Web applications had requirements to support functionality of this nature, Patterns and idioms developed to shoehorn pre- and postprocessing into existing implementations of the specification. The result was code that follows this AntiPattern. Habits. Another typical cause is developers not staying current and continuing to build applications with old approaches.

SAQ 2.2 What would be an appropriate material for liquid cell windows if an aqueous solution at pH 7 is to be examined