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Smolak, L & Levine, MP (1996) Adolescent transition and the development of eating problems In L Smolak, MP Levine & R Striegel-Moore (Eds), The Developmental Psychopathology of Eating Disorders Implications for Research, Prevention, and Treatment (pp 207 235) New Jersey: Lawrence Erlbaum Associates Stein, DJ (1997) Internet addiction, Internet psychotherapy American Journal of Psychiatry, 153, 890 Steinhausen, HC (1995) Children of alchoholic parents: A review European Child and Adolescent Psychiatry, 4, 143 152 Striegel-Moore, RH, Silberstein, LH & Rodin, J (1986) Toward an understanding of risk factors for bulimia American Psychologist, 41, 246 263 Striegel-Moore, RH (1993) Etiology of binge eating: A developmental perspective In CG Fairburn & GT Wilson (Eds) Binge Eating Nature, Assessment and Treatment (pp 144 172) New York: Guilford Press Stokols, D (1996) Translating social ecological theory into guidelines for community health promotion.

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d(oi , oj ) between the objects oi and oj . PAM is more robust than Forgy s variant of k-clustering because it minimizes the sum of the dissimilarities instead of the sum of the squared errors. The algorithm has two distinct phases: the building phase and the swapping phase. The building phase aims to construct a set L of selected objects, L S. The set or remaining objects is denoted by R; clearly, R = S L. We begin by determining the most centrally located object. The quantities Qi = n dij are computed starting from the matrix D. The most j=1 central object oq is the determined by q = arg mini Qi . The set L is initialized as L = {oq }. Suppose now that we have constructed a set of L of selected objects and |L| < k. We need to add a new selected object to the set L. To do this, we need to examine all objects that have not been included in L so far, that is, all objects in R. The selection is determined by a merit function M : R N. To compute the merit M(o) of an object o R, we scan all objects in R distinct from o. Let o R {o} be such an object. If d(o, o ) < d(L, o ), then adding o to L could bene t the clustering (from the point of view of o ) because d(L, o ) will diminish. The potential bene t is d(o , L) d(o, o ). Of course, if d(o, o ) d(L, o ) no such bene t exists (from the point of view of o ). Thus, we compute the merit of o as M(o) =

This problem is best understood by considering an example with binary variables: consider the graph and the probability distributions shown in Figure 112 (it is pABC pF DE and pBD pCE ) Suppose that we observe A = 1 Since this enforces B = C and thus D = E, we should obtain, as an inference result, P(F = 0) = 0 and P(F = 1) = 1 However, the marginal distributions on the individual variables B and C do not change due to this observation (they are still P(B = 0) = P(B = 1) = P(C = 0) = P(C = 1) = 05) As a consequence no information is transmitted to the right half of the network, leading to the incorrect result P(F = 0) = P(F = 1) = 05 This also illustrates the potential pitfalls of loopy propagation (cf Section 42.

The decibel is used to quantify gains and losses across a telecommunication network The most common and ubiquitous end-to-end highway across that network is the voice channel (VF channel) A voice channel conjures up in our minds an analog channel, something our ear can hear The transmit part (mouthpiece) of a telephone converts acoustic energy emanating from a human mouth to electrical energy, an analog signal At the distant end of that circuit an audio equivalent of that analog energy is delivered to the receiver (earpiece) of the telephone subset with which we are communicating This must also hold true for the all-digital network When dealing with the voice channel, there are a number of special aspects to be considered by the transmission engineer In this section we will talk about these aspects regarding frequency response across a well-de ned voice channel.

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