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of the base level class (or classes). The relationship of two classes can be clari ed by expressing it in a sentence. If the sentence B is a kind of A, accurately represents the relationship between two classes, then class B can be derived from A. For example, an apple is a kind of fruit, so an apple class should be derived from a fruit class. However, if the sentence B is a part of A is clearer than B is a kind of A, a using relationship should be used, not inheritance. For example, seeds are a part of a fruit, so their classes should be related with a using relationship. In general, no more than two levels of inheritance should be used. Before deciding on using multiple inheritance, alternatives that take into account the effect that multiple inheritance will have on the system, especially its maintainability, should be considered. Because changes to high-level base classes affect all child classes, deep inheritance trees (greater than about two ancestors) should be avoided. Requirements Related to This Policy: There is no speci c requirement related to this policy. This is strictly a design issue. Automation of the Policy Veri cation: Once the code is implemented, the depth of the inheritance can be veri ed by a static analysis tool. Policy for Code Reuse Critical Attribute: Reusability of the code Possible Problems: Interfaces between existing legacy modules may not be compatible with modules designed for the new application. Speci c Design Rules for Reuse: Only the modules marked for reuse in existing applications shall be reused. When designing new modules, interfaces to the existing code shall be designed rst, and then the new module s functionality. Requirements Related to This Policy: All new system features will be compatible with the speci ed components of legacy systems. Automation of the Policy Veri cation: The legacy code marked for reuse can be automatically veri ed. Interface design has to be inspected manually. 6.2.3 Applying Design Patterns
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*Discrete or bounded continuous: E [hA ] = q A. Continuous: P (qn A ) = 1, A H where P > 0 (Harris). Positive recurrence: mean return time to A is bounded.
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In the past, competition has been based primarily on products, whether they are tangible ones, from manufacturers, or intangible ones, like an auto loan, from service providers. But now there is a higher form of competition coming in the future, beyond just selling products. For suppliers to retain customers for life, it will be essential that they collaborate in some form that is mutually bene cial to their customers, their employees, and their shareholders. Many believe that with today s integrated information tools, suppliers have realized that superior service, speed, and convenience are the key to retaining customers and increasing market share. However, acceleration in new product development and innovation are powering every company s competitors ability to achieve service, speed, and convenience. So it is a race. The real challenge for suppliers is to nd different and additional ways to create customer value. Lots of people talk about this, but few have discovered what it takes. Some competitors will add value by focusing on the customer s experience. This will apply somewhat less when commodities are purchased, but even those purchases can be spiced with an experience. Adding value to a customer s purchasing experience requires a deep understanding of what a customer values. Very few companies or consultants have moved into this territory. Strategy consultants rarely touch this area and prefer to stick to giving traditional marketing advice. Consumer product companies may not know the ultimate consumer s psyche. In the end, services will be added to products, and unique services will be tailored to individuals. To relate back to PM, strategy maps and scorecards will guide the marketing functions. Obvious KPIs will be retention rates, acquisition
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First, the complex mechanism of a VCG auction can be hard for bidders to understand. It is not intuitive and bidders may well not follow the proper strategy. Secondly, it is very hard to implement. This is because each bidder must submit an extremely large number of bids, and the auctioneer must solve a NP-complete optimization problem to determine the optimal partition. No fast (polynomial-time) solution algorithm is available for NPcomplete problems, so the winner determination problem can be unrealistically dif cult to solve. There are several ways that bidding can be restricted so that the optimal partitioning problem becomes a tractable optimization problem (i.e. one solvable in polynomial time). Unfortunately, these restrictions are rather strong, and are not applicable in many cases of practical interest. One possibility is to move the responsibility for solving the winner determination problem from the seller to the bidders. Following a round of bidding, the bidders are challenged to nd allocations that maximize the social welfare. 14.2.3 Double Auctions Another interesting type of multi-unit auction is the double auction. In this auction, there are multiple bidders and sellers. The bidders and sellers are treated symmetrically and participate by bidding prices (called offers and asks ) at which they are prepared to buy and sell. These bids are matched in the market and market-clearing prices are generated by some rule. The double auction is one of the most common trading mechanisms and is used extensively in the stock and commodity exchanges. In an asynchronous double auction, also called a Continuous Double Auction (CDA), the offers to buy and sell may be submitted or retracted at any time. A public order book lists, at each time t, the currently highest buy offer, b.t/, and currently lowest sell offer, s.t/. As soon as b.t/ s.t/, a sale takes place, and the values of b.t/ and s.t/ are updated. Today s stock exchanges usually work with CDAs, and they have also been used for auctions conducted on the Internet. In a synchronized double auction, all participants submit their bids in lock-step and batches of bids are cleared at the end of each period. Most well-known double auction clearing mechanisms make use of a generalization of Vickrey Clarke Groves mechanism. For example, suppose that there are m sell offers, s1 s2 sm and n buy offers, b1 b2 bn . Then the number of units that can be traded is the number k such that sk bk , but skC1 > bkC1 . The speci cation of the buy and sell prices are a bit complicated. However, it is interesting to study them, to see once more how widely useful is the VCG mechanism. We suppose that the market maker receives all the offers and asks, and then computes k, as above, and a single price pb to be paid by each buyer and a single price ps to be received by each seller. In general, pb > ps . The buyer price is pb D maxfsk ; bkC1 g. Thus, pb is the best unsuccessful offer, as long as this is more than the greatest successful ask; otherwise, it is the greatest successful ask. To see that pb is indeed an implementation of the VCG mechanism, assume that all participants bid their valuations. Let V be the sum of the valuations placed on the items by those who hold them at the end of the auction. Thus P P V D i si C i k .bi si /. For any i, let V .i/ be de ned as V , but excluding the valuation placed by i on any item that he holds at the end of the auction. Suppose i is a successful bidder. If i did not participate and sk < bkC1 , then the best unsuccessful bidder becomes successful and obtains value bkC1 ; so V .i/ increases by bkC1 . However, if sk > bkC1 , then the best unsuccessful bidder has not bid more than sk and so seller k retains the item for which his valuation is sk and which he would have sold to buyer i; thus V .i/ increases
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PRAM, PMBOK, and RAMP are a useful representative sample of alternative RMP frameworks that the reader may wish to relate to the SHAMPU process elaborated in the rest of this book. Any other process framework of interest could be characterized in relation to these three to gain some insights about the relationships. Although some alternatives may require a quite new approach to comparison, the basic issues will be similar. Examples that may be of interest of which we are aware include: Construction Industry Research and Information Association (CIRIA) (Godfrey, 1996), CAN/CSA-Q850-97 (1997), ICAEW (1999), AS/NZS 4360 (1999), BS6079-3 (2000), AIRMIC, ALARM and IRM (2002), and Of ce of Government Commerce (OGC, 2002). No doubt we have missed some, and others will be forthcoming. Williams (1995) provides a useful review of earlier research, Williams (2003) some more recent views. RMPs developed and promoted by professional organizations, like PRAM, RAMP and PMBOK, have an important role to play in the development of best practice, for a number of reasons. For example, they can bring together experts with different experience, synthesize that experience in a unique way, and tailor completely general best practice approaches to particular types of context, which facilitates constructive detail. However, they have limitations imposed by the need for group consensus. Like all views, different RMPs need to be subjected to constructive critique from alternative perspectives, and our collective best interests are served if these RMPs support each other and move toward common basic concepts. The comparisons provided by this chapter were much more dif cult to analyse than the authors anticipated, and they will sur-
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After you click Edit, you ll see all the settings for a pro le. We ve divided con guration settings into two groups: basic and advanced. Basic settings are things everyone can, and should, do. Advanced settings deal with many of the situations that come up in the requirements gathering process.
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classes have only been publicly available since S60 3rd Edition FP2.
Creating a Technical Position
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Sell tomorrow at Lowest(Low,shortLength) stop;
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The equivalent single-phase diagram is shown in Figure 10-42.
MURTHY, XIE, and JIANG Weibull Models MYERS and MONTGOMERY Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Second Edition MYERS, MONTGOMERY, and VINING Generalized Linear Models. With Applications in Engineering and the Sciences NELSON Accelerated Testing, Statistical Models, Test Plans, and Data Analyses NELSON Applied Life Data Analysis NEWMAN Biostatistical Methods in Epidemiology OCHI Applied Probability and Stochastic Processes in Engineering and Physical Sciences OKABE, BOOTS, SUGIHARA, and CHIU Spatial Tesselations: Concepts and Applications of Voronoi Diagrams, Second Edition OLIVER and SMITH In uence Diagrams, Belief Nets and Decision Analysis PALTA Quantitative Methods in Population Health: Extensions of Ordinary Regressions PANKRATZ Forecasting with Dynamic Regression Models PANKRATZ Forecasting with Univariate Box-Jenkins Models: Concepts and Cases PARZEN Modern Probability Theory and Its Applications PENA, TIAO, and TSAY A Course in Time Series Analysis PIANTADOSI Clinical Trials: A Methodologic Perspective PORT Theoretical Probability for Applications POURAHMADI Foundations of Time Series Analysis and Prediction Theory PRESS Bayesian Statistics: Principles, Models, and Applications PRESS Subjective and Objective Bayesian Statistics, Second Edition PRESS and TANUR The Subjectivity of Scientists and the Bayesian Approach PUKELSHEIM Optimal Experimental Design PURI, VILAPLANA, and WERTZ New Perspectives in Theoretical and Applied Statistics PUTERMAN Markov Decision Processes: Discrete Stochastic Dynamic Programming RAO Linear Statistical Inference and Its Applications, Second Edition RENCHER Linear Models in Statistics RENCHER Methods of Multivariate Analysis, Second Edition RENCHER Multivariate Statistical Inference with Applications RIPLEY Spatial Statistics RIPLEY Stochastic Simulation ROBINSON Practical Strategies for Experimenting ROHATGI and SALEH An Introduction to Probability and Statistics, Second Edition ROLSKI, SCHMIDLI, SCHMIDT, and TEUGELS Stochastic Processes for Insurance and Finance ROSENBERGER and LACHIN Randomization in Clinical Trials: Theory and Practice ROSS Introduction to Probability and Statistics for Engineers and Scientists ROUSSEEUW and LEROY Robust Regression and Outlier Detection RUBIN Multiple Imputation for Nonresponse in Surveys RUBINSTEIN Simulation and the Monte Carlo Method RUBINSTEIN and MELAMED Modern Simulation and Modeling RYAN Modern Regression Methods RYAN Statistical Methods for Quality Improvement, Second Edition SALTELLI, CHAN, and SCOTT (editors) Sensitivity Analysis SCHEFFE The Analysis of Variance SCHIMEK Smoothing and Regression: Approaches, Computation, and Application SCHOTT Matrix Analysis for Statistics SCHUSS Theory and Applications of Stochastic Differential Equations SCOTT Multivariate Density Estimation: Theory, Practice, and Visualization
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