New computational approaches for branching processes in population biology

分支过程是种群生物学中强大的建模工具。它们描述了个体如何根据特定的概率定律生活和繁殖，并可用于回答广泛的人口相关问题。这个项目的目的是开发新的算法方法，一类易处理的分支过程称为马尔可夫二叉树。继矩阵分析方法，它将提供新的结果，有效地估计模型参数，以及随机环境对种群动态的影响。这些结果将用于研究进化和保护生物学中的重要问题，从而建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.