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Although the initial deployment of a wireless PBX can be expensive, it pays o in improved employee productivity and enhanced customer (internal and external) satisfaction. For wireless communications within a single corporate network, it is prudent to consider an IP-based virtual network to support ubiquitous and uniform terminals and services. In this environment, network segments must interoperate seamlessly with public and private carriers. Additional operations, administration, and maintenance costs for managing such a worldwide virtual network also need careful analysis. Other prime issues include discovering the called mobile unit or terminal for completing a connection request and maintaining the integrity of the o ered connections or calls in progress. Systems typically accomplish this by paging, broadcasting, and/or mobility tracking or management methods. Tracking methods include



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The Bayesian approach to probability turns these assumptions around In Bayesian statistics, the parameters are considered to be random variables, and the data are considered to be known The parameters are regarded as coming from a distribution of possible values, and Bayesians look to the observed data to provide information on likely parameter values Let represent the parameters of the unknown distribution Bayesian analysis requires elicitation of a prior distribution for , called the prior distribution, p( ) This prior distribution can model extant expert knowledge, if any, regarding the distribution of For example, churn1 modeling experts may be aware that a customer exceeding a certain threshold number of calls to customer service may indicate a likelihood to churn This knowledge can be distilled into prior assumptions about the distribution of customer service calls, including its mean and standard deviation.





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been discussed, the slide shows a routed packet using direct and indirect routing. This is regardless of the routing protocol. In this slide, we can see that a PC (endstation A) is trying to pass a datagram to a host machine, called host D. The host machine is one hop (one router) away. The IP layer of the PC (endstation A) knows that it must use a router (the source and destination network addresses are different), and will use RIP or the default gateway to determine the IP address of the router to use. Upon determining the router s physical address, it will physically (MAC layer) address the packet to the router at port B. The source and destination IP addresses in the IP header of this datagram will be the PC as the source, and the destination IP address as the host. The source (PC) and final destination (the host) IP addresses will be embedded into the IP header and will not change throughout the routing of this datagram.

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If expert knowledge regarding the prior distribution is not available, Bayesian analysts may posit a noninformative prior, which assigns equal probability to all values of the parameter For example, the prior probability of both churners and nonchurners could be set at 05 using a noninformative prior (Note that if this assumption does not seem reasonable, you must be applying your expert knowledge about churn modeling!) Regardless, because the eld of data mining often encounters huge data sets, the prior distribution should be dominated by the overwhelming amount of information to be found in the observed data Once the data have been observed, prior information about the distribution of can be updated, by factoring in the information about contained in the observed data This modi cation leads to the posterior distribution, p( |X), where X represents the entire array of data.

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