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8




           Observed Data and Graphical

           Representation









           Referring to Figure 1.1 in Chapter 1, we are concerned in this and subsequent
           chapters with step D !  E of the basic cycle in probabilistic modeling, that is,
           parameter estimation and model verification on the basis of observed data. In
           Chapters 6 and 7, our major concern has been the selection of an appropriate
           model (probability distribution) to represent a physical or natural phenom-
           enon based on our understanding of its underlying properties. In order to
           specify the model completely, however, it is required that the parameters in the
           distribution be assigned. We now consider this problem of parameter estima-
           tion using available data. Included in this discussion are techniques for asses-
           sing the reasonableness of a selected model and the problem of selecting a
           model from among a number of contending distributions when no single one
           is preferred on the basis of the underlying physical characteristics of a given
           phenomenon.
             Let us emphasize at the outset that, owing to the probabilistic nature of the
           situation, the problem of parameter estimation is precisely that – an estima-
           tion  problem.  A  sequence of observations,  say n in  number,  is a  sample of
           observed values of the underlying random variable. If we were to repeat the
           sequence  of  n  observations,  the  random  nature  of  the  experiment  should
           produce a different sample of observed values. Any reasonable rule for
           extracting  parameter  estimates  from  a  set  of  n  observations  will  thus  give
           different estimates for different sets of observations. In other words, no single
           sequence of observations, finite in number, can be expected to yield true
           parameter values. What we are basically interested in, therefore, is to obtain
           relevant information about the distribution parameters by actually observing
           the underlying random phenomenon and using these observed numerical
           values in a systematic way.

          Fundamentals of Probability and Statistics for Engineers  T.T. Soong  2004 John Wiley & Sons, Ltd
          ISBNs: 0-470-86813-9 (HB) 0-470-86814-7 (PB)



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