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Obviously, a range of probabilities, rather than a single number, is more likely to be correct and less troublesome to obtain Although mathematical formulae exist allowing for the use of ranges rather than discrete assessments of risk, such approaches are exceedingly complex and unsuitable for use by the practitioner Fortunately, this problem is neatly solved by what is known as Monte Carlo simulation Simply stated, Monte Carlo simulation utilizes random numbers to determine the actual outcome of the various uncertainties composing a problem Each complete simulation represents one possible outcome of the problem The simulation is repeated many times to create a statistical analysis of all possible outcomes of a problem Thus, unlike the simple analysis of our example (which produced a single number representing the expected value of all possible outcomes), Monte Carlo simulation yields a distribution of all possible outcomes with their corresponding probability of occurrence.

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Rather than purporting to tell what will happen, Monte Carlo simulation specifies how likely any possible outcome actually is Such a simulation also offers a fine opportunity to perform a sensitivity analysis..

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TSG SA WG2 tries if necessary to align the new target between the involved parties TSG SA WG1 and TSG SA are kept informed of the overall schedule It was also in the model, it was identi ed as a task for TSG SA, TSG SA WG1 and TSG SA WG2 to ensure early involvement of TSG SA WG3 (working group responsible for security) to ensure that the potential security requirements, service requirements and the architectural requirements are aligned and communicated to the TSGs and their WGs In order for TSG T and its subgroups to plan and perform its horizontal tasks on conformance testing and mobile station capabilities, it was foreseen to invite TSG T to evaluate the potential impact of a new feature Also work on the horizontal tasks is required to be included in the overall work plan.

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540 Microarray data analysis (Continued) bootstrap approach to gene selection 128 129 bootstrapping analysis 137 bottom-up clustering 135 classical feature selection (CFS) 138 140 cluster validation 137 138 clustering of microarray data 135 correlation among gene expression pro les 135 distance of gene expression pro le clusters 137 empirical Bayes analysis 128 false discovery rate (FDR) control 129 131 functional annotation of genes 130 gene ontology (GO) 130 133, 139 140 hierarchical clustering 135 identi cation of differentially expressed genes 125, 129 kernel method 137 Kruskal Wallis test 127 128, 139 140 Mann Whitney U-test 127 mixture model approach 137 nonparametric statistical approaches 127 one-way analysis of variance (ANOVA) 126 128 parametric statistical approaches 126 Pearson correlation coef cient 137 principal component analysis (PCA) 138 random forest (RF) clustering 135 regression model approaches 128 RF predictor 135 sample t-test 126 self-organizing map (SOM) 135 shrinkage-based similarity procedure 137 signi cance analysis of microarray (SAM) 128 Student s t-test 126 supervised methods for functional annotation 134 support-vector machines (SVM) model 134, 140 unsupervised methods for functional annotation 133 volcano plot 126 Wilcoxon rank-sum test 127

We argue that a model for the primary prevention of eating disorders alone is not warranted. Rather the focus should be to enable people to increase control of their own health (WHO, 1986). This salutogenic perspective implies a focus on factors which increase the likelihood that people manage to stay healthy, rather than on disputable risk factors for a particular illness. Educational policy and the school system form the basis and the premise for health promotion and primary prevention. For instance, the most recent version of the Norwegian prevention material (B rresen, 1999) follows this more general, health promotion approach. It includes issues like self-esteem, self-assertion, positive and negative coping strategies, stress management, puberty and what it means to grow up, and the developmental stressors of adolescents. The cultural obsession with slenderness is, of course, also emphasised, because it is important to address not only behavioural

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This is a standard result from statistics when two random processes in which each can result in one of two states are correlated (i.e., have a joint probability of occurrence J).

traders complying with the trading service are implemented by C O RBA objects themselves. Their interfaces are fully specified in C O RBA/IDL. The specification also includes a service type definition language and proposes a structure for a service type repository. The C O RBA Trading service standardizes a constraint definition language for use in queries. The trader also determines policies and preferences to control the wayoffer sequences are computed by the query. The C O RBA Trading service supports federated traders. The purpose of this section is to give a concise overview of how C O RBA implements the concepts and requirements that were outlined in the previous section.