arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2202.07084math.PR
收藏

变化环境下分支树的凝聚点过程

Coalescent point process of branching trees in varying environment

Airam Blancas, Sandra Palau

AI总结:

本文针对变化环境下 Galton-Watson 过程驱动的种群谱系重构问题,通过反例指出已有方法缺乏 Markov 性质,并提出一种向量值 Markov 过程以有限信息重构谱系,同时证明了线性分数后代分布下变量的独立同分布性。

摘要:

考虑当前时刻的一个任意大的种群,其起源于过去某个未指定的任意大的时间点,其中同一代的个体在时间上向前独立繁殖,具有相同的后代分布,但在不同代之间可能发生变化。换言之,繁殖由变化环境下的 Galton-Watson 过程驱动。当前一代在时间上向后的谱系由凝聚点过程 $(A_i, i\geq 1)$ 唯一确定,其中 $A_i$ 是个体 $i$ 和 $i+1$ 之间的凝聚时间。通常,该过程不是 Markov 的。在恒定环境下,Lambert 和 Popovic(2013)提出了一个点测度的 Markov 过程来重构凝聚点过程。我们提出了一个反例,证明他们的过程不具有 Markov 性质。本文的主要贡献是提出一个向量值 Markov 过程 $(B_i,i\geq 1)$,以有限信息对每个 $i$ 实现重构谱系的目标。此外,当后代分布为线性分数时,我们证明变量 $(A_i, i\geq 1)$ 是独立同分布的。

英文摘要:

Consider an arbitrary large population at the present time, originated at an unspecified arbitrary large time in the past, where individuals in the same generation reproduce independently, forward in time, with the same offspring distribution but potentially changing among generations. In other words, the reproduction is driven by a Galton-Watson process in a varying environment. The genealogy of the current generation backwards in time is uniquely determined by the coalescent point process $(A_i, i\geq 1)$, where $A_i$ is the coalescent time between individuals $i$ and $i+1$. In general, this process is not Markov. In constant environment, Lambert and Popovic (2013) proposed a Markov process of point measures to reconstruct the coalescent point process. We present a counterexample where we show that their process does not have the Markov property. The main contribution of this work is to propose a vector valued Markov process $(B_i,i\geq 1)$, that reach the goal to reconstruct the genealogy, with finite information for every $i$. Additionally, when the offspring distributions are lineal fractional, we show that the variables $(A_i, i\geq 1)$ are independent and identically distributed.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑