Statistical solutions for the open challenges of integrated population models.

在动物保护的世界里，获得诸如种群大小、存活率和繁殖力等特征的准确估计是很重要的。从历史上看，当收集到一个物种的种群数据时，会创建一个模型来估计这些重要特征。但是，当在同一物种上收集多个不同的数据类型时，找到一种方法来组合这些数据集以利用共享信息是很有用的。这是通过综合人口模型实现的。然而，这些模型仍然存在一些问题。例如，当存在多种数据类型时，选择最适合数据的最佳模型变得很有挑战性。此外，模型拟合算法需要较长的运行时间，并且难以实现，这使得许多生态学家无法使用它们。需要更有效的解决方案，使综合人口模型对更广泛的生态学家有用。我的博士研究的是适应和改进当前的模型拟合算法，保持封闭形式解决方案的使用，从而提高效率。研究领域;统计生态学，计算统计学与奥索大学合作。

In the world of animal conservation, it is important to obtain accurate estimates of features such as population size, survival rates, and fecundity. Historically, when data are collected on a species' population, a model is created to estimate these important features. However, when multiple different data types are collected on the same species, it is useful to find a way to combine these datasets to leverage the shared information. This is achieved through Integrated Population Models. However, there are still some issues with these models. For instance, choosing the best model that fits the data best becomes challenging when in the presence of multiple data types. Additionally, model-fitting algorithms that require a long running time and are difficult to implement make them less accessible for many ecologists. More efficient solutions are needed to make Integrated Population Models useful for a broader audience of ecologists. My PhD is looking at adapting and improving current model fitting algorithms, maintaining the use of closed form solutions, and thus efficiency.Research areas; Statistical Ecology, Computational StatisticsIn partnership with Olso University.