Estimation of Parameters

  • Josef Honerkamp
Part of the Graduate Texts in Physics book series (GTP)


In Chap. 2 we introduced random variables, their corresponding densities, and quantities characterizing these densities. We have also seen how to compute with random variables. Up to now, we have avoided a discussion about the interpretation of the probability density. Of course, what we always had in mind is the so-called relative frequency interpretation of the probability density: One assumes that many realizations of the random variable exist. Then a histogram where the relative frequencies of the measured values in so-called bins, i.e., small intervals, are plotted against the intervals themselves, should converge with increasing number of realizations to the probability density.


Unbiased Estimator Confidence Region Belief Function Monte Carlo Integration Normal Random Number 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Authors and Affiliations

  • Josef Honerkamp
    • 1
  1. 1.Fakultät für Mathematilk und PhysikAlbert-Ludwigs-Universität FreiburgFreiburgGermany

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