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The visual clue of having a more extreme location than most of the other rules can be used to convey an intuitive handling to the user Given the four rule measures support, recall, confidence, and lift (used as explained above) the user investigates the class variable and tries to identify rules that are located close to the upper right-hand side corner We conclude this chapter with a prospect on the visualization of model change over time A model induced from an entire database (as is assumed to be the case throughout this book) represents, in a way, an aggregated model Most databases support timestamps and thus the insertion order of the data can be used to create different subsets of cases of the database wrt time If a considerable model change occurred during the period for which the data was collected, we may also expect a model change.

However, when the evaluation of evidence requires people to consider multiple sources of uncertainty, they sometimes make more extreme judgments than Bayesian models dictate (Gettys, Kelly and Peterson, 1973) Furthermore, research on mock jurors evaluations of statistical evidence has revealed a number of distinct errors in assessing conditional probabilities that lead people to deviate from Bayesian norms (Thompson, 1989; Kaye and Koehler, 1991) For example, when evaluating evidence that the suspect in a criminal case has the same blood type as the perpetrator, and that this blood type is found in X % of the population, people sometimes conclude that the probability the suspect is the perpetrator, given the evidence, is necessarily equal to 1/X , an error that Thompson and Schumann (1987) labeled the prosecutor s fallacy On other occasions people commit the defense attorney s fallacy of giving such evidence no weight at all (Thompson, 1989).

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And people perform poorly, relative to Bayesian norms, when combining information on the frequency of a matching characteristic (such as a blood group) with information on the error rate of the test showing the match (Koehler, Chia and Lindsey, 1995; Schklar and Diamond, 1999) Courts are becoming sensitive to the potential for such errors In the United Kingdom, two criminal convictions have been reversed in cases in which prosecutors and the government s expert witnesses made arguments consistent with the prosecutor s fallacy when characterizing the value of DNA evidence for the jury (Regina v Deen, 1994; Balding and Donnelly, 1994) Bayesian models also fail to capture the process of human decision-making Jurors appear to evaluate evidence in a trial not by sequential updating (as posited by a Bayesian model) but by constructing plausible narratives that might account for the evidence (Hastie and Pennington, 1992; Pennington and Hastie, 1991a,b).

If this change is reflected in different network structures, the proposed visualization cannot be applied However, for short periods within which we may conjecture only minor model changes in terms of parameter deviations, we can use the proposed visualization method to indicate the changes as follows: for every rule (corresponding to an entry of the potential table of interest) a time series of the rule properties is calculated These are used to create an animation where each step of the time series is used as a key frame Between two key frames we simply interpolate to create a smooth animation Figure 106 sketches this idea A rule is evaluated at three points in time It shows that the set of covered cases is growing with time (as the support and.

According to the story model , proposed by Pennington and Hastie, various narratives (including those explicitly advanced by the parties) are evaluated according to how coherently and comprehensively they can account for the facts The most plausible narrative is then assessed vis-` -vis relevant legal standards to determine the verdict Forensic a identi cation evidence, such as evidence that the suspect and perpetrator share a rare blood type, may also be evaluated in non-Bayesian fashion Koehler (2001) has recently suggested that jurors evaluate such evidence according to the ease with which they can imagine other individuals who might match and has shown that mock jurors judgments are affected in ways inconsistent with Bayesian norms by factors that in uence the availability of coincidental match exemplars ..

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SECURE COMMUNICATION IN DISTRIBUTED SENSOR NETWORKS (DSN)

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Lees, 1996). It is possible that a similar attitude prevails among some lawyers with regard to children so that an aware child is prima facie a less trustworthy complainant than an ignorant child. The equation of ignorance with innocence in the following comment is interesting in this context.

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