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LemmaStatement: AI-adaptedProof: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-14
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Killed and absorbed kernels are probability kernels

Statement

For every probability kernel K and measurable D, the absorbed kernel KDabs and killed kernel KDkill of the preceding definition are probability kernels on (E,E) and (EΔ,EΔ) respectively.

Facts & Assumptions

Given: A probability kernel K and DE.

[F1]

The absorbed and killed candidates, including the cemetery sigma-algebra, are the formulas in Killed and absorbed transition kernels.

[F2]

A probability kernel has probability-measure sections and measurable evaluation functions. (Measure kernel and probability kernel)

Proof

1.1

Fix xE. If xD, the absorbed section in [F1] is δx; if [F1, F2] xDc, it is K(x,). Hence every section is countably additive, has empty-set mass zero and total mass one. For fixed AE, its evaluation is 1D(x)1A(x)+1Dc(x)K(x,A), a measurable function by [F2]. Thus KDabs is a probability kernel.

F1F2
1.2

Fix xD. The killed section is the restriction [F1, F2] AK(x,AD) on E, plus an atom of mass K(x,Dc) at Δ. It is countably additive and its total mass is K(x,D)+K(x,Dc)=1. If xDc{Δ}, the section is δΔ. Thus all killed sections are probability measures, including D= and D=E.

F1F2
2.1

Fix BEΔ, put A=BE, and let [F1, F2, step 1.2] ε=1B(Δ). On E the killed evaluation is 1D(x){K(x,AD)+εK(x,Dc)}+1Dc(x)ε, which is E-measurable by [F2]. Its value at the measurable singleton {Δ} is ε, so the full evaluation is EΔ-measurable. Therefore KDkill is a probability kernel. The empty and full target sets give respectively zero and one in every case. No choice principle is used.

F1F2step 1.2

Depends on

Used by

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Sources