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Food and beverage inventories will vary based on the geographical area and the size of the city or towns within a given geographical area. Food turnover on the average will normally vary between two and four times a month. Beverage turnover varies from one to four times per month. Individual operations should determine in each case the turnover rate appropriate to the area in which the establishment operates (since there are major exceptions to these guidelines), and

Remark Note from (2.130) that Pe = 0 implies that H (X|Y ) = 0, as intuition suggests. Proof: We rst ignore the role of Y and prove the rst inequality in (2.130). We will then use the data-processing inequality to prove the more traditional form of Fano s inequality, given by the second inequality in (2.130). De ne an error random variable, E= 1 0 if X = X, if X = X. (2.133)

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a person has a low trait level. The likelihood that a person with a moderate trait level could pass all the items is higher, but the response pattern is most likely for a person with a high trait level. Finding the IRT trait level for a response pattern requires a search process instead of a scoring procedure. Trait levels typically are estimated by a maximum likelihood method; speci cally, the estimated trait level for person s maximizes the likelihood of his or her response pattern given the item properties. Finding maximum likelihood estimates of trait levels requires a computer program that (a) computes the likelihoods of response patterns under various trait levels and (b) conducts a search process for the trait level that gives the highest likelihood. To illustrate the concept of searching for a trait estimate that yields the highest likelihood, consider Figure 17.6. This gure shows the likelihoods for four response patterns under various hypothetical abilities. Each likelihood shown is computed by multiplying the probabilities for the ve items. In turn, each item probability is computed using a particular IRT model (in this case, the Rasch model in Equation 17.8) with known item parameters and a hypothetical ability. In Figure 17.6, likelihoods are computed for 13 different hypothetical abilities for each response pattern. The ability that gives the highest likelihood would be the maximum likelihood estimate. For response pattern 1, for example, the estimate would be 2.0. It should be noted that computer programs like BILOG (Mislevy & Bock, 1990), PARSCALE (Muraki & Bock, 1997), and RUMM (Sheridan, Andrich, & Luo, 1996) apply complex numerical procedures to nd maximum likelihood estimates. Also, estimation can be conducted using prior information, such as a population ability distribution.

VoIP IMPLEMENTATION CHALLENGES The openness and exibility of the IP and the World Wide Web (WWW) have steered the development of a variety of devices and techniques for implementation of VoIP. However, the same openness, ubiquity, and exibility to support open APIs and to carry multiple types of tra c (e.g., real- and non-realtime audio, video, and data streams) have also created many service reliability and security related concerns. Some of these issues are discussed below, along with a few desirable features of the feasible solutions.

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Weller, E B, 266 Weller, R A, 266 Wellner, A S, 336 Welsh, G S, 238 Welsh, R S, 449, 451 Wertheimer, M, 253 West, R W, 440 West, S G, 236 Weston, D, 14 Whalen, C K, 337 Wheeler, M, 466 Whipple, J L, 303, 304, 313, 314 Whipple, K, 305, 306 Whitaker, C, 65 White, J, 464 White, M, 134 Wicherski, M M, 453 Widiger, T A, 192, 193, 195 Wiens, A N, 195, 557 Wigal, T, 337, 338 Wiggins, J G, 448, 449, 450, 451, 458, 486 Wiggins, J S, 242 Wilens, T E, 337, 338 Wilfley, D E, 95 Wilkinson, G S, 229 Williams, C L, 239 Williams, D, 345, 346 Williams, G, 105, 130 Williams, J B.

APIT [23] is quite a bit different from the beacon-based distributed algorithms described so far. APIT uses a novel area-based approach, in which nodes are assumed to be able to hear a fairly large number of beacons. However, APIT does not assume that nodes can range to these beacons. Instead, a node forms some number of beacon triangles, where a beacon triangle is the triangle formed by three arbitrary beacons. The node then decides whether it is inside or outside a given triangle by comparing signal-strength measurements with its nearby

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