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The remainder of this chapter is devoted to showing how to do transient analysis with the source traf c odel We suggest focusing on the future time-dependent mean conditional on the present state The present state of each level process consists of the level and age (elapsed holding time in that level) Because of the anticipated large number of sources, the actual bandwidth process should be closely approximated by its mean, by the law of large numbers (LLN) As in Duf eld and Whitt [14], the conditional mean can be thought of as a deterministic uid approximation; for example, see Chen and Mandelbaum [8] Since the withinlevel variation process has mean zero, the within-level variation process has no effect on this conditional mean Hence, the conditional mean of the aggregate bandwidth process is just the sum of the conditional means of the component level processes.

Item Selection Item selection subsystems can vary greatly Some testing organizations prefer to use simple item database queries, random item selection strategies, or strati ed random item sampling of content categories within statistically determined bins to select the test items for each test form Other organizations are transitioning toward using more sophisticated automated test assembly (ATA) software ATA nvolves the use of formal mathematical optimization procedures that include linear programming algorithms, network ow algorithms, and various deviation-based heuristics (Armstrong, Jones, & Kunce, 1998; Luecht, 1998a, 2000; Stocking & Swanson, 1998; Timminga, 1998; van der Linden, 1998) Using these types of algorithm or heuristic, ATA seeks to satisfy a particular mathematical goal, called the objective function , subject to any number of constraints on content and other test item attributes For example, ATA algorithms can build a test form to achieve a particular level of test dif culty or to achieve a prescribed amount of precision in some region of the score scale, subject to also meeting almost any number of content requirements or other test speci cations such as minimum test length, word counts, reading levels, statistical impact on minority and majority groups, DIF, enemy items that cannot appear on the same test form, etc These latter speci cations are the ATA constraints There are three functional requirements for an item selection subsystem especially if ATA is employed First, the subsystem needs to provide a means of extracting eligible item data from the item database This extraction process requires using a database query to pull down the relevant statistical and content-related data elds for a subset of the entire item database Second, the item selection subsystem needs a second, interactive database application to store and manipulate the various ATA statistical targets and constraints (ie the test speci cations) used during item selection Each ATA build becomes a project that may be stored, modi ed, and re-run in the future Finally, the subsystem requires a computer program sometimes called an ATA engine to actually select the items for the test forms Depending on the type of ATA engine used, the extracted item data, the statistical targets, and the test assembly constraints may need to be further reformatted in some type of mathematical programming syntax Unfortunately, despite the growing popularity of ATA for CBT, userfriendly ATA software engines are still not widely available6 There are ATA software systems in operational use that incorporate all three of the functions noted above However, these systems either use proprietary corporate-owned software or rely on public-domain software developed for research rather than large-scale operational uses (see, for example, Luecht,.

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Unlike the more elementary M =G=I model considered in Duf eld and Whitt [14], however, the conditional mean here is not available in closed form In order to rapidly compute the time-dependent conditional mean aggregate demand, we exploit numerical inversion of Laplace transforms It follows quite directly from the classical theory of semi-Markov process that explicit expressions can be given for the Laplace transform of the conditional mean More recently, it has been shown that numerical inversion can be an effective algorithm; see Abate et al [1] For related discussions of transient analysis, design and control, see s 13, 16, and 18 in this volume 173 OUTLINE OF THE CHAPTER In Section 174, we show that the Laplace transform f the mean of the transient conditional aggregate demand can be expressed concisely This is the main enabling result for the remainder of the chapter.

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In Section 17.9 we show how long-range dependence n the level process arises through heavy-tailed level-holding-time distributions. Finally, we draw conclusions in Section 17.10. In Visual Studio .NET Using Barcode encoder for .NET .Using Barcode creator for Visual Studio .NET Control to generate, create barcode image in NET applications.Related: Generate Barcode SSRS Library, Barcode Generator Crystal VB.NET , Excel Barcode Generation Library

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