New computational approaches for branching processes in population biology

分支过程是种群生物学中强有力的建模工具。它们描述了个体如何根据特定的概率法则生活和繁殖，并可用于回答一系列与人口有关的问题。该项目旨在为一类易处理的分支过程开发新的算法方法，称为马尔可夫二叉树。在矩阵分析方法之后，它将在模型参数的有效估计以及随机环境对种群动态的影响方面提供新的结果。这些结果将被用于研究进化和保护生物学中的重大问题，从而确立所开发技术的相关性。

Branching processes are powerful modelling tools in population biology. They describe how individuals live and reproduce according to specific probability laws, and can be used to answer a wide range of population-related questions. This project aims to develop new algorithmic methods for a tractable class of branching processes called Markovian binary trees. Following a matrix analytic approach, it will deliver new results on the efficient estimation of model parameters, and on the effects of random environments on population dynamics. These results will be used to study significant problems in evolutionary and conservation biology, thereby establishing the relevance of the developed techniques.