In the most general meaning, a distributed computer system is identified with computer network. There are two problems with this identification. One concerns the word “computer”. Even the origins of such systems encompassed not only computers but also devices far from being computers, like missile launchers and other military objects (a brief historical outline is in Chap. 2). Let alone today’s networks that connect things of professional and everyday usage: one can hardly say that the so-called intelligent refrigerator is a computer. The other problem concerns the word “distributed”. The main feature of such architectures is lack of shared physical memory, where processors intercommunicate by message passing through data links (channels) of arbitrary length and bandwidth. So, in this meaning, a motherboard of transputers (Sect. 5.3 in Chap. 5), processors interacting by data packet exchange inside one machine, all are distributed systems too. But on the other hand, a computer surrounded by simple terminals for data input and output, multi-access computer or workstations with direct access to memory of a mainframe, are systems categorized as not distributed in the aforesaid meaning, though distributed in the common parlance, because separated spatially. Nonetheless, the distinguishing characteristic of distributed computer systems as the research and engineering domain, is inter-computer communication by message passing based on networks. However, the design, technical solutions, and appli- cation of distributed systems and general (“generic”) networks, are not identical. The conceptual difference lies in their destination. A distributed system is being often constructed as a computation environment for specific class of applications, for a company of certain activity profile, for a corporation or education center, whereas a network is a universal tool for data transmission not limited to particular applications. Thus, the constructional difference consists in softw

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Contents List of symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 Statistical definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1 Probability density function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Statistical moments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.1 Expected value. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.2 Variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.3 Covariance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 Working with samples from a distribution . . . . . . . . . . . . . . . . . . 9 2.3.1 Sample mean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3.2 Sample variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3.3 Sample covariance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4 Statistics of random fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4.1 Sample mean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4.2 Sample variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4.3 Sample covariance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.4.4 Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.5 Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.6 Central limit theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3 Analysis scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1 Sc

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