论文标题
在非线性模型的参数歧管上构建精确的置信区域
Constructing Exact Confidence Regions on Parameter Manifolds of Non-Linear Models
论文作者
论文摘要
使用信息几何形状的数学框架，我们引入了一种新颖的方法，该方法允许人们有效地确定非线性参数化模型的同时置信区的确切形状。此外，我们还展示了如何从对确切的置信区域的详细知识中构建模型预测周围的置信带，而几乎没有额外的计算工作。我们使用宇宙学和流行性建模中的推断问题来体现我们的方法。通过信息几何。
Using the mathematical framework of information geometry, we introduce a novel method which allows one to efficiently determine the exact shape of simultaneous confidence regions for non-linearly parametrised models. Furthermore, we show how pointwise confidence bands around the model predictions can be constructed from detailed knowledge of the exact confidence region with little additional computational effort. We exemplify our methods using inference problems in cosmology and epidemic modelling. An open source implementation of the developed schemes is publicly available via the InformationGeometry.jl package for the Julia programming language.
