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because it is far more numerically accessible The connection between multifractals and wavelets arises from the fact that the increments involved in the study of the local regularity of a sample path can be seen as simple examples of wavelet coef cients [52] It has therefore been proposed heuristically [52] to replace increments by wavelet coef cients in the partition functions and shown theoretically that, in some cases, the multifractal formalism can be based directly on wavelet coef cients [16, 42, 60] For the Legendre multifractal spectrum, this amounts to using wavelet-based partition functions that exhibit, for small scales, power-law behavior: jTX o a; t jq dt % az o;q q=2 , a 3 0 This last relation can be thought of as a generalization of P1 to statistics of order both above and below 2.



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In addition, it is important to understand that even though the relation describes a property of a single (typical) realization, it deals directly with the object z o; q central to the description of the scaling, and not to an estimator of it This is in contrast to selfsimilar processes, for example, and the fractal class of the previous paragraph, where the fundamental scaling relations and exponents are de ned at the level of the ensemble Such a change of perspective is meaningful for multifractals as almost all realizations yield a common function z q Finally, let us note that more re ned wavelet-based partition functions have been proposed to overcome various dif culties arising in signal processing; the reader is referred to Bacry et al [16] and Muzy et al [52] The third example is that of multiplicative cascades, a paradigm introduced by Mandelbrot [51] in 1974.

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It involves a recursive procedure whereby an initial mass is progressively subdivided according to a geometric rule and assigned to subsets of an initial set, typically an interval It provides a powerful tool to de ne multifractal processes and was originally considered as a natural synthesis procedure for them Indeed, cascade-based methods of generating multifractals have been the preferred option thus far in teletraf c applications (see 15) However, the in nitely divisible model proposed by Castaing et al [21] shows that multiplicative cascade processes can also very effectively model scaling phenomena in other cases, even where the scaling is barely observable in the time domain Again, the wavelet tool has proved useful for the analysis of such situations, as comprehensively detailed by Arneodo et al [14, 15] This tool has been applied, for instance, in the study of turbulence [22, 63] 223.

Realistically, it is unlikely that we have hit on every single point that readers will run into while implementing critical systems We do believe, however, that our general advice will be applicable to many specific situations Some readers may begrudge the lack of simple, universal answers in this book There are two reasons for this One is that the issues that arise at each site, and for each computer system, are different It is unreasonable to expect that what works for a 10,000-employee global financial institution will also work for a 10-person law office We offer the choices and allow the reader to determine which one will work best in his or her environment The other reason is that after 15 years of working on, with, and occasionally for computers, I have learned that the most correct answer to most computing problems is a rather unfortunate, It depends.

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