Titlebook: Random Point Processes in Time and Space; Donald L. Snyder,Michael I. Miller Textbook 1991Latest edition Springer-Verlag New York, Inc. 19

adduction

CLEFT

尊严

Doubly Stochastic Poisson-Processes,cesses in this chapter are also obtained by randomizing the intensity of the Poisson process. Here, however, this randomization is not through self excitation but, rather, by an “outside” process so that the paths of the resulting intensity process are not known given only the point process.

CAMP

利用

Point and Counting Processes: Introduction and Preliminaries, Each event is represented in the model by an idealized point to be conceived of as identifying the position of the event. If . denotes the continuum space, then a realization of a random point process on . is a set of points in .. The number and variety of phenomena for which this type of stochasti

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Poisson Processes,ere the points occur is a one-dimensional, semiinfinite, real line. While there is no mathematical reason to do so, we refer to this space as “time” because temporal phenomena seem to predominate in applications. The study of temporal Poisson-processes permits many of the properties of Poisson proce

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Compound Poisson-Processes,s model can account for the occurrence times of the pulses. However, these times alone do not reflect all of the significant features. The amplitudes of the pulses exhibit wide variation and have a strong influence on a radio receiver operating at low frequencies. Even a first-approximation model fo

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SEMI

Doubly Stochastic Poisson-Processes,process itself. In this way, the intensity is transformed into a random process having paths that are known exactly given the point process. Point processes in this chapter are also obtained by randomizing the intensity of the Poisson process. Here, however, this randomization is not through self ex

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1431-8482 en to point processes on multidimensional spaces, especially to pro­ cesses in two dimensions. This reflects the tremendous increase that has taken place in the use of point-process models for the description of data from which images of objects of interest are formed in a wide variety of scientific