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Conditional intensity: A powerful tool for modelling and analysing point process data
Author(s) -
Diggle Peter J.
Publication year - 2021
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/anzs.12331
Subject(s) - point process , mathematics , point (geometry) , function (biology) , process (computing) , intensity (physics) , conditional probability distribution , statistics , computer science , physics , geometry , quantum mechanics , evolutionary biology , biology , operating system
Summary The conditional intensity function of a spatial point process describes how the probability that a point of the process occurs ‘at’ a particular point in its carrier space depends on the realisation of the process in the remainder of the carrier space. Provided that the point process is simple, the conditional intensity determines all of the properties of the process, in particular its likelihood function. In this paper, we review the use of the conditional intensity function in the formulation of point process models and in making inferences from point process data, giving separate consideration to temporal, spatial and spatiotemporal settings. We argue that the conditional intensity function should take centre‐stage in spatiotemporal point process modelling and analysis.