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and neural networks A few approaches may be used with categorical and continuous responses All approaches have advantages and disadvantages, and the different approaches often require the data to conform to certain assumptions The major approaches are summarized in Table 44 In selecting which approach to use, other practical considerations may need to be considered In the telecommunication example, the marketing department needed a classi cation model to predict the binary response variable churn; however, the department also wanted to prioritize the results so they can focus their efforts on customers most likely to switch In this situation, the logistic regression method might be a good candidate since it generates a prediction for the binary variable churn and calculates the probability of switching Different modeling approaches operate in different ways and selecting the best method requires an understanding of the data and how the different methods operate Figure 4.

This subsection deals with practical calculation of third-order intermodulation products in a power amplifier, given the intermodulation products in the case of two equal reference carriers (a specification usually given by the tube manufacturer). For generalities on intermodulation effects, the reader is referred to 2, 2.1.5.2. In the case of two carriers (N=2) an approximate formula for the calculation of the power of each third-order intermodulation product IM (in watts, first type) is:

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2 illustrates different types of approaches In chart A, a single independent variable is plotted against a single response variable Because a linear relationship exists between the two variables, they can be modeled using a linear regression method In contrast, chart B indicates a nonlinear relationship where the data either needs to be transformed or needs to be used with a method that is capable of modeling these relationships In chart C, the data used to build the model was divided into regions based on speci c values or ranges of the independent variables In this example, two independent variables are used to illustrate how the data can be divided into regions (no response variable is shown).

S (t)

When making a prediction, the approach assigns an observation into a speci c region based on values or ranges for the independent variables, and a prediction is made based on training data in the region, such.

where the expectation is taken with respect to the LFM numeraire P( , Tn) rather than the LSM numeraire Cn,N. Notice that since the swap rate, given in (13.34), can be expressed in terms of spanning forward rates at time Tn, then the above expectation depends on the joint distribution of the spanning forward rates Fn+1(Tn), Fn+2(Tn), . . . , FN(Tn) and thus the forward rate correlations impact the price unlike in cap/ oor pricing.11 To price a swaption using Monte Carlo, we need to generate M realizations of

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Sometimes we need to calculate an annualized return for cumulative periods that are not exact multiples of a year. To calculate annualized returns for such periods, we count the actual number of days in the cumulative period and divide it by 365.25 to calculate an annualized equivalent: Annualized return

involves much more than an optical image projected on the retina of the eye and interpreted photographically by the brain. The human eye is primarily a device that gathers information about the outside world. Its focusing lens throws a minute inverted image onto a dense mosaic of light-sensitive receptors, which convert the patterns of light energy into chains of electrical impulses that the brain will interpret (figure 1.3). The simplest way to form an image is not with a lens, however, but with a pinhole. In figure 1.4, a ray from each point of the object reaches only a single point on the

I highlighted (in bold) the actual options (lines beginning with # are comments). Save the file when you have entered the information. Because the configuration file could contain information such as the server password that you don t want everyone to see, you should restrict access to the file. For example, you could make it so that the user running the server utility is the only one who can read it, as follows (replacing username with the user s name that will run the server):

FIGURE AP3.2-16

The questions we have asked till now have focused on issues that will help us to determine how many CPUs we will need and how fast they need to be. To generate our estimate, we will use the rule of thumb that each concurrent user requires 10 MHz of CPU speed. Applying this rule means that our 4,000-user timecard application that supports 80 concurrent users can run on a modest single 800-MHz CPU. This assumes that the Web application server and EJB container and databases are each on their own independent servers. In this case of 80 concurrent users, it is very likely that we will deploy the application on the same type of system that we developed it on. On the other hand, if we had 40,000 users, 800 of them concurrent at peak times, we might select a system with 4 CPUs, each running at 2 GHz. How this processing power is distributed through the layers of the architecture will be discussed in the Hardware Scaling section later in this chapter.

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