The Concentration Process

  • Jan Grandell
Part of the Lecture Notes in Statistics book series (LNS, volume 30)


Up to now we have essentially been interested in the random variable c(O) and not in the stochastic process c(t). The reason is, cf. the discussion in section 3, that the models considered describe c(t) while the interesting concentration is cE(t). In the derivation we have, on the other hand, often used the stationarity of c(t), which is a stochastic process property. Thus it is mathematically highly natural to consider the stochastic process c(t). When the source is deterministic we might consider c(t) in an Eulerian sense, cf. again section 3. In that case it might, also from the point of view of applications, be relevant to consider c(t) as a stochastic process. For purely mathematical reasons we shall, however, sometimes let the source be random.


Markov Model Covariance Function Exact Distribution Independent Increment Point Process Model 
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Copyright information

© Springer-Verlag Berlin Heidelberg 1985

Authors and Affiliations

  • Jan Grandell
    • 1
  1. 1.Department of MathematicsThe Royal Institute of TechnologyStockholmSweden

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