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LLC is a protocol developed by the IEEE 802.2 working group and provides three different types of service: LLC Type 1 (LLC-1) Used for connectionless services. LLC Type 2 (LLC-2) Used for connection-oriented services. LLC Type 3 (LLC-3) Used for acknowledgments in conjunction with connectionless services. LLC-1 is used for connectionless services. It is a best-effort POP QUIZ delivery, providing none of the bells and whistles (for instance, The most popular and most often used ow control). LLC-1 provides . CSMA/CD protocol is multiplexing services to the upper layers. LLC-29 is used for connection-oriented services. Because it serves the connection-oriented operations, it does support the bells and whistles (for example, ow control, error control and recovery, call setup, call management, and call termination). LLC-3, which is seldom used, acknowledges frame delivery in a connectionless environment.10

PHP is a programming language for building dynamic, interactive Web sites. As a general rule, PHP programs run on a Web server, and serve Web pages to visitors on request. One of the key features of PHP is that you can embed PHP code within HTML Web pages, making it very easy for you to create dynamic content quickly. What exactly does the phrase dynamic, interactive Web sites mean A dynamic Web page is a page whose contents can change automatically each time the page is viewed. Contrast this with a static Web page, such as a simple HTML file, which looks the same each time it s displayed (at least until the page is next edited). Meanwhile, an interactive Web site is a site that responds to input from its visitors. A Web forum is a good example users can post new messages to the forum, which are then displayed on the site for all to see. Another simple example is a contact us form,

A completely different source of error are modeling errors If the expression for the expectations of the observations and the function used by the experimenter are different parametric families of functions, this will lead to systematic errors in the estimated parameters For example, if the expectations of observations made are the sum of a multiexponential function and a constant representing background radiation while the function used by the experimenter is only the multiexponential function, the parameters of a wrong function will be estimated Modeling errors are not to be considered systematic errors in the observations They are caused by the experimenter On the other hand, if a background is routinely included in the model although it is known that there is no background present, the model is correct but the background parameter is equal to zero and superfluous This is an example of overparameterization.

All active calls in the system are processed once each radio resource indication period, as illustrated in Figure 7.11. If the ongoing connection is CS, the simulator collects its throughput, increases the active connections counter and releases the call in case it lasted longer then the corresponding call duration period (see Table 7.3). For each packet-

Overparameterization increases the standard deviation of the estimates of the remaining parameters, which, in our example, are the amplitudes and the decay constants The terminology thus introduced enables us to give the following overview of the book In 2, we discuss parametric models of observations After a short introduction in Section 21, this discussion starts in Section 22 with deterministic (that is, nonstatistical) parametric models This is followed, in Section 23, by an analysis of traditional parameter estimation methods which are based on the assumption that such deterministic observations really exist This analysis reveals the need for the statistical parametric models introduced in Section 24 In the same section, the term expectation model is introduced for the parametric function that describes the expectations of the observations Describing observations as stochastic variables implies that they are defined by probability (density) functions These are the subject of 3.

After a short introduction in Section 31, we define in Section 32 some preliminary notions such as the covariance matrix of a set of stochastic variables and the Fisher score vector Then, in the Sections 3335, three important joint distributions of sets of observations are defined and discussed: the multivariate normal, the Poisson, and the multinomial distribution These belong to an important general class called exponential families of distributions This class is introduced in Section 36 Exponential families will be used throughout the book and will lead to considerable generalizations and simplifications Statistical properties of the Fisher score vector are discussed in Section 37 In Section 38, complex stochastic variables are introduced These are important in a number of disciplines in applied science dealing with complex parameters or complex observations.