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(Note: in this book no distinction is made between ethics and morality . All variations of each word are used interchangeably.) If you want to understand the importance of ethics, you should rst be clear what ethics is not ethics is not a single body of knowledge about what is right and wrong and it is not something that anyone can be expert in. Yes, people can study for degrees in technical ethical theory but this does not make them any more or less ethical in everyday life. Our social world is far more intricate and enigmatic than this. Because ethical re ection is meant to give answers to questions about how men and women should act, the scope of ethical concern is in nitely varied. This diversity can be dissected into many different types, the analysis of which can become very complicated (as any survey of texts in moral philosophy will show) since most types contain a host of further sub-divisions (see Six). These distinctions can be interesting to professional philosophers and their students, who enjoy debating their respective merits, but they have little impact on daily life. To see this, compare everyday and technical ethics.

46 Harsanyi JC Games with incomplete information played by Bayesian players, I, II, III Manage Sci 1967;14:159 182, 320 332, 468 502 47 Hart S, Schmeidler D Existence of correlated equilibria Math Oper Res 1989;14(1): 18 25 48 Hirsch M, Papadimitriou CH, Vavasis S Exponential lower bounds for nding brouwer xpoints J Complexity 1989;5:379 41 49 Johnson DS, Papadimitriou CH, Yannakakis M How easy is local search J Comp Sys Sci 1988;17(1):79 100 50 Kaporis A, Spirakis P The price of optimum in stackelberg games Proceedings of the 18th Annual ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2006); 2006 p 19 28 51 Kearns M, Littman M, Singh S Graphical models for game theory Proceedings of the 17th Conference on Uncertainty in Arti cial Intelligence; 2001 p 253 260 52 Kearns M, Ortiz L Algorithms for interdependent security games.

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Moody s observes that the input nancial ratios are highly nonnormally distributed and consequently adds another layer to the probit regression by introducing transformation functions derived empirically on the nancial ratios. The dependence of ve-year cumulative default probabilities was obtained in a univariate nonparametric analysis. ( Nonparametric estimation refers to a collection of techniques for tting a curve when there is little a priori knowledge about its shape. Many nonparametric procedures are based on using the ranks of numbers instead of the numbers themselves.) This process determines a transformation function T for each ratio xi. These transformation functions were obtained from Moody s proprietary private rm defaults database. Thus, the full probit model estimated in RiskCalc is Prob(Default) = N[ T (x)] where is the row vector of 10 weights to be estimated, T(x) is the column vector of the 10 transformed nancial ratios, and N[ . . . ] is the cumulative standard normal distribution function. Private Firm Model Moody s KMV While this discussion is appropriately located under the heading of the models that rely on nancial statement data, it may be easier to understand this model if you rst read the description of the Moody s KMV public rm model (i.e., Credit Monitor5 and CreditEdge) in the next section of this chapter. The public rm model was developed rst; and the Private Firm Model was constructed with the same logic. The approach of the Private Firm Model is based on dissecting market

It can therefore be tested in the presence of an ipsilateral 7th nerve lesion; the jaw-jerk, like any other stretch re ex, is exaggerated in the presence of an upper motor neurone lesion In the case of the jaw-jerk, the lesion must be above the level of the trigeminal motor nucleus in the pons In patients with upper motor neurone signs in all four limbs, an exaggerated jaw-jerk is sometimes helpful in suggesting that the lesion is above the pons, rather than between the pons and the mid-cervical region of the spinal cord; pathology in the cavernous sinus affects only the ophthalmic and maxillary branches of the trigeminal nerve, as the mandibular branch has dived through the foramen ovale, behind the cavernous sinus Similarly, orbital pathology affects only the ophthalmic branch, since the maxillary branch has exited the skull through the foramen rotundum posterior to the orbit Figure 8.

tmp = numpy.zeros(n) for j in range(n): s = 0.0 if w[j] <> 0: for i in range(m): s += u[i, j]*b[i] s /= w[j] tmp[j] = s x = numpy.zeros(n)

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