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#### Functional AnalysisMathematical PhysicsProbabilityTheoretical Physicsmathscidoc:1702.12002

Journal of Statistical Physics, 152, (4), 769-786, 2013.8
Consider an age-dependent, single-species branching process defined by a progeny number distribution, and a lifetime distribution associated with each independent particle. In this paper, we focus on the associated inverse problem where one wishes to formally solve for the progeny number distribution or the lifetime distribution that defines the Bellman-Harris branching process. We derive results for the existence and uniqueness (the identifiability) of these two distributions given one of two types of information: the extinction time probability of the entire process (extinction time distribution), or the distribution of the total number of particles at one fixed time. We demonstrate that perfect knowledge of the distribution of extinction times allows us to formally determine either the progeny number distribution or the lifetime distribution. Furthermore, we show that these constructions are unique. We then consider “data” consisting of a perfectly known total number distribution given at one specific time. For a process with known progeny number distribution and exponentially distributed lifetimes, we show that the rate parameter is identifiable. For general lifetime distributions, we also show that the progeny distribution is globally unique. Our results are presented through four theorems, each describing the constructions in the four distinct cases.
inverse problems, branching processes
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```@inproceedings{pak-wing2013identifiability,
title={Identifiability of Age-Dependent Branching Processes from Extinction Probabilities and Number Distributions},
author={Pak-Wing Fok, and Tom Chou},
url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20170214095917149996441},
booktitle={Journal of Statistical Physics},
volume={152},
number={4},
pages={769-786},
year={2013},
}
```
Pak-Wing Fok, and Tom Chou. Identifiability of Age-Dependent Branching Processes from Extinction Probabilities and Number Distributions. 2013. Vol. 152. In Journal of Statistical Physics. pp.769-786. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20170214095917149996441.
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