On the Stationary Measure for Markov Branching Processes

A previous study determined criteria ensuring that a probability distribution supported in positive integers is the limiting conditional law of a subcritical Markov branching process. It is known that there is an close connection between the limiting conditional law and the stationary measure of the...

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Main Author: Anthony G. Pakes
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Mathematics
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Online Access:https://www.mdpi.com/2227-7390/13/11/1802
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author Anthony G. Pakes
author_facet Anthony G. Pakes
author_sort Anthony G. Pakes
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description A previous study determined criteria ensuring that a probability distribution supported in positive integers is the limiting conditional law of a subcritical Markov branching process. It is known that there is an close connection between the limiting conditional law and the stationary measure of the transition semigroup. This paper revisits that theme of by seeking tractable criteria ensuring that a sequence on positive integers is the stationary measure of a subcritical or critical Markov branching process. These criteria are illustrated with several examples. The subcritical case motivates consideration of the Sibuya distribution, leading to the demonstration that members of a certain family of complete Bernstein functions, in fact, are Thorin–Bernstein. The critical case involves deriving a notion of the limiting law of population size given that extinction occurs at a precise future time. Examples are given, and some show an interesting relation between stationary measures and Hausdorff moment sequences.
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spelling doaj-art-6cd5fd613d0e4a7ca0dc205b721e12cd2025-08-20T02:32:37ZengMDPI AGMathematics2227-73902025-05-011311180210.3390/math13111802On the Stationary Measure for Markov Branching ProcessesAnthony G. Pakes0Department of Mathematics and Statistics, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, AustraliaA previous study determined criteria ensuring that a probability distribution supported in positive integers is the limiting conditional law of a subcritical Markov branching process. It is known that there is an close connection between the limiting conditional law and the stationary measure of the transition semigroup. This paper revisits that theme of by seeking tractable criteria ensuring that a sequence on positive integers is the stationary measure of a subcritical or critical Markov branching process. These criteria are illustrated with several examples. The subcritical case motivates consideration of the Sibuya distribution, leading to the demonstration that members of a certain family of complete Bernstein functions, in fact, are Thorin–Bernstein. The critical case involves deriving a notion of the limiting law of population size given that extinction occurs at a precise future time. Examples are given, and some show an interesting relation between stationary measures and Hausdorff moment sequences.https://www.mdpi.com/2227-7390/13/11/1802Markov branching processlimiting conditional lawstationary measureinfinite divisibilityThorin–Bernstein function
spellingShingle Anthony G. Pakes
On the Stationary Measure for Markov Branching Processes
Mathematics
Markov branching process
limiting conditional law
stationary measure
infinite divisibility
Thorin–Bernstein function
title On the Stationary Measure for Markov Branching Processes
title_full On the Stationary Measure for Markov Branching Processes
title_fullStr On the Stationary Measure for Markov Branching Processes
title_full_unstemmed On the Stationary Measure for Markov Branching Processes
title_short On the Stationary Measure for Markov Branching Processes
title_sort on the stationary measure for markov branching processes
topic Markov branching process
limiting conditional law
stationary measure
infinite divisibility
Thorin–Bernstein function
url https://www.mdpi.com/2227-7390/13/11/1802
work_keys_str_mv AT anthonygpakes onthestationarymeasureformarkovbranchingprocesses