17 research outputs found

    A framework for the local information dynamics of distributed computation in complex systems

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    The nature of distributed computation has often been described in terms of the component operations of universal computation: information storage, transfer and modification. We review the first complete framework that quantifies each of these individual information dynamics on a local scale within a system, and describes the manner in which they interact to create non-trivial computation where "the whole is greater than the sum of the parts". We describe the application of the framework to cellular automata, a simple yet powerful model of distributed computation. This is an important application, because the framework is the first to provide quantitative evidence for several important conjectures about distributed computation in cellular automata: that blinkers embody information storage, particles are information transfer agents, and particle collisions are information modification events. The framework is also shown to contrast the computations conducted by several well-known cellular automata, highlighting the importance of information coherence in complex computation. The results reviewed here provide important quantitative insights into the fundamental nature of distributed computation and the dynamics of complex systems, as well as impetus for the framework to be applied to the analysis and design of other systems.Comment: 44 pages, 8 figure

    Hacer estimaciones estadísticas

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    The notion of statistical estimation both in terms of point and interval is described. The criteria of a good estimator are noted. The procedures to calculate the intervals for the mean, proportions and the difference among two means as well as the confidence intervals for the probable errors in statistics are provided

    Tamaño optimo de la muestra

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    The basics of sample size estimation process are described. Assuming the normal distribution, the procedures for estimation of sample size for the mean; with and without knowledge of the population variance, and population proportion are noted. Sample size for more than one population feature is also given

    Analisis de correlación canonica (ACC) e investigacion cientifica.

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    The concept of Analysis of Canonical Correlation (ACC) is given. The basic conditions, initial questions, and main objectives are provided. The fundamentals of ACC design and the adjustments are touched upon. Field application of ACC is highlighted. The intricacies involving the profiling, validation, and redundant variables of the method are discussed. Finally, the statistical significance and theoretical interpretation of the model are explained
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