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New York: Milbank Memorial Fund Linney, J A (1986) Court ordered school desegregation: Shuf ing the deck or playing a different game In E Seidman & J Rappaport (Eds), Rede ning social problems (pp 259 274) New York: Plenum Press Linney, J A (2000) Assessing ecological constructs and community context In J Rappaport, & E Seidman (Eds), Handbook of community psychology (pp 647 668) New York: Kluwer Academic/Plenum Press Long, B E (1968) Teaching psychology to children American Psychologist, 23(9), 691 692 Long, B B (1989) The mental health association and prevention Prevention in Human Services, 6(2), 5 44..

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To introduce Bayesian networks, we shall use the clothing purchase example, illustrated by the Bayes net in Figure 5.8. Suppose that a clothes retailer operates two outlets, one in New York and one in Los Angeles, each producing sales throughout the four seasons. The retailer is interested in probabilities concerning three articles of clothing in particular: warm coats, business shirts, and Bermuda shorts. Questions of interest include the fabric weight of the article of clothing (light, medium, or heavy) and the color of the article (bright, neutral, or dark). To build a Bayesian network, there are two main considerations: (1) the dependence relationship among the variables of interest, and (2) the associated local probabilities. The retailer has ve variables: season, location, clothing purchase, fabric weight, and color. What is the dependence relationship among these variables For example, does the season of the year depend on the color of the clothes purchased Certainly not, since a customer s purchase of some bright clothing doesn t mean that spring is here, for example, although the customer may wish it so. In fact, the season of the year does not depend on any of the other variables, so we place the node for the variable season at the top of the Bayes network, which indicates that it does not depend on the other variables. Similarly, location does not depend on the other variables, and is therefore placed at the top of the network. Since the fabric weight and the color of the clothing are not known until the article is purchased, the node for the variable clothing purchase is inserted next into the network, with arcs to each of the fabric weight and color nodes.

FIGURE 23-17

Layout view enables you to see the relative positions of the controls on the report s surface, as well as the margins, page headers and footers, and other report details. The main constraint of Layout view is that you can t make ne adjustments to a report s design unless you put the report in Design view. Layout view is primarily intended to allow you to adjust the relative positions of controls on the report and is not meant for moving individual controls around on the report. For example, you can delete any item by clicking on the item and pressing the Delete key, or move the item to another location by dragging it on the report s area. While in Layout view, you can also right-click on any control and select Properties from the shortcut menu. The Property Sheet allows you to modify the default settings for the selected control.

anchorage-dependent, are only loosely attached and do not spread even when cultured on tissue culture-treated surfaces When maintained in suspension the cells readily form large, loose aggregates comprising up to hundreds of cells In the presence of dexamethasone (01 10 M), the cells remain similarly rounded and loosely attached and grow clonally in clusters Of utmost importance, any cultures of PC12 cells should never become overcrowded and should be subcultured at 40 50% con uence PC12 cells secrete neurotrophic factors (eg, NGF and bFGF) and also other, as yet not fully characterized, differentiating factors, which when left to accumulate in the culture medium will a) induce PC12 cell differentiation toward the neuronal phenotype and b)give rise to a morphologically and functionally distinct phenotype of small, well-spread, anchorage-dependent cells If these cells appear in our cultures, we abstain from any further use of the cells.

Keeping that in mind, what we will do in this chapter is lay down a few of the more signi cant cloud computing concepts building on the growing industry consensus, where it applies and illustrate the most essential characteristics.

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