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Killed and absorbed kernels are probability kernels
Statement
For every probability kernel and measurable , the absorbed kernel and killed kernel of the preceding definition are probability kernels on and respectively.
Facts & Assumptions
Given: A probability kernel and .
The absorbed and killed candidates, including the cemetery sigma-algebra, are the formulas in Killed and absorbed transition kernels.
A probability kernel has probability-measure sections and measurable evaluation functions. (Measure kernel and probability kernel)
Proof
Fix . If , the absorbed section in [F1] is ; if [F1, F2] , it is . Hence every section is countably additive, has empty-set mass zero and total mass one. For fixed , its evaluation is a measurable function by [F2]. Thus is a probability kernel.
Fix . The killed section is the restriction [F1, F2] on , plus an atom of mass at . It is countably additive and its total mass is . If , the section is . Thus all killed sections are probability measures, including and .
Fix , put , and let [F1, F2, step 1.2] . On the killed evaluation is which is -measurable by [F2]. Its value at the measurable singleton is , so the full evaluation is -measurable. Therefore is a probability kernel. The empty and full target sets give respectively zero and one in every case. No choice principle is used.
Depends on
Used by
- Absorbing gambler's-ruin chain Example
Dependency tree · two levels
4 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Durrett, Probability: Theory and Examples, Section 5.2 (standard reference, not scraped)