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Papangelou conditional intensity on a finite window
Definition
Let , , and be as in Finite-window spatial observations and configurations. Let be a specified probability law on ; denotes a fixed exterior configuration when the model uses a boundary condition, and is omitted when it does not. For this fixed boundary convention, a jointly measurable function is a Papangelou conditional intensity relative to for this law and boundary convention; the notation displays the fixed boundary label but is not an additional variable in its measurable domain. Omit when there is no exterior condition or when the fixed convention is clear from context. The defining identity holds when, for every nonnegative product-measurable , The sum runs over atom occurrences, so deletes one occurrence; if is used, it is fixed on both sides. The identity is the definition of the Papangelou intensity used on this page. It does not assert that every finite-window law has such a function.
The deletion operation and the resulting measurable random sum use Deleting one occurrence from a locally finite counting measure is measurable. On the occurrence domain, the deletion-composition is measurable; extend it by zero off that measurable domain and integrate against the evaluation kernel . This integral is the displayed sum, and its measurability follows from the deletion map and Measurability of integration against a kernel, as used in the deletion lemma.
Depends on
- Finite-window spatial observations and configurations
- Probability measures and probability spaces
- Expectation of a nonnegative or integrable random variable
- The nonnegative Lebesgue integral
- Extended-real-valued measurable functions
- The product sigma-algebra and its finite iterates
- Deleting one occurrence from a locally finite counting measure is measurable
- Measurability of integration against a kernel
Used by
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Sources
- Adrian Baddeley, Spatial Point Processes and their Applications, complete lecture notes (standard reference, not scraped)
- Günter Last and Mathew Penrose, Lectures on the Poisson Process, author-hosted 2017 version (standard reference, not scraped)