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policy-based management and (b) the plethora of policy tools available today. According to Saperia [2002], a policy implies . . . a predetermined action pattern that is repeated by an entity whenever certain system conditions appear. The IETF de nes a policy as a de nite goal, course, or method of action to guide and determine present and future decisions [Westerinen et al., 2001], but also as a set of rules to administer, manage, and control access to network resources [Moore et al., 2001]. In order to provide some background to the reader, this section provides an overview of some of the terminology used when discussing policies for network management. Policy-based management has been discussed in the literature for close to two decades, but it really began to gain widespread attention in the late 1990s, when the IETF (Internet Engineering Task Force) formed the Policy Framework Working Group. The goals of this working group were to de ne an architecture and an information model for policy-based management of QoS in IP networks. At the same time, the Distributed Management Task Force (DMTF) was working on developing a wide range of information models for applications including network and policy management. The Policy Working Group within the DMTF (also known as the Service

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development phase, developing software or products, without the appropriate planning in place. When the project ran into problems, it was often too late to turn back, and these projects often failed in some way. In cases where no planning was done, it became extremely difficult to reverse such a situation. It is essential that a considerable amount of time be spent planning a project. It is extremely likely that the project manager will be required to double-check the project planning in order to reflect last-minute changes and unforeseen circumstances taking shape on the project. A project manager can quickly determine how difficult a task he or she will face based on the results of the initial planning. Figure 5.1 illustrates a basic planning process.

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Multiplying the square matrix M - I by its inverse on the left-hand side would give the identity matrix, I (by de nition), whereas the right-hand side would reduce to 0: 1 0 R = 0 0 1 F 0 R = 0 F 0 This is unhelpful as we are left with the trivial solution that R and F are equal to 0. To overcome this problem we need to assume that the matrix M - I does not have an inverse. This is true if the determinant of the matrix is equal to 0. This assumption can then be used to nd a value for : s1,2 fs0,1 =0 (4.16)

More recently, Sepkoski s fossil data set has been reanalysed to reveal 62 and 140 Myr periodicity (Rohde & Muller 2005) Therefore, this fossil data set, spanning the entire duration of the Phanerozoic, illustrates both deterministic (periodic) and stochastic (randomwalk) processes..

Unfortunately, marginalization of this density loses some of the structural information because the axes do not line up well. In fact, if we look at the marginal posteriors from this density, much of the valley seems to disappear, particularly for 1 . So HPD intervals of the full set of marginal densities do not contain the same information as the complete joint distribution. This is observable in Figure 4.5, where the sense of bimodality is almost gone for 1 and observable but indeterminant for 2 . There are several solutions to this problem. First, one could present the results graphically in higher dimensions such that important features are evident. Second, it is possible to use Givens rotations or the Household transformations (Thistead 1988, Chap. 3). Unfortunately, guring out the rotations of the axes that give the most informative picture is dif cult in higher dimensions and usually requires considerable trial and error. One way to get an idea of where such features exist in complex posteriors is to run the EM algorithm from many starting points and observe the location and structure of the modes. 4.5.3 Inference in the Presence of Instability Occasionally, despite following all of the recommendations discussed in this chapter, one may not be sure whether one s answers are accurate: Perturbation tests may continue to show sensitivity to noise, or different platforms may give you substantively different answers. Despite one s best attempts to research the properties of each algorithm and software package used, and to examine all relevant diagnostics, a set of competing solutions or a range of possible answers may still exist. What can be done when faced with several competing answers

It could be argued that this starting point means we are talking about risk and uncertainty management or just uncertainty management (including risk management), not risk management . That is the direction we are taking (Chapman and Ward, 2002; Ward and Chapman, 2003), but the term project risk management is too well established to be replaced widely in the near term and is retained for this book. Our publisher was understandably reluctant to change the title of a second edition while the rst edition (Chapman and Ward, 1997) had sales that were very healthy and stable, one example of the inertia involved. But increasing the emphasis on opportunity management in an uncertainty management context is a key feature of this edition relative to the rst.

Equations (3.138) and (3.139) can be written in matrix format as A B = G H + y A B (3.141)

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