Latent factor longitudinal social networks and bayesian melding for modelling biological network structures, and longitudinal/spatial bent cables for modelling natural phenomena

许多自然系统和现象都是高度复杂的。为了充分了解它们，科学家们开发了定量技术来解决潜在结构的不同方面。这类技术中使用的可观测数量包含不确定性，由于收集生物数据的方案，不确定性往往很大。如果这种不确定性没有得到适当的解决，我们对自然的理解就会受到限制。统计建模可以解释这种不确定性。对于这一建议，我开发了这样的模型，重点放在两个主要领域：生物网络结构和所谓的变点现象。

Many natural systems and phenomena are highly complex. To gain an adequate understanding of them, scientists develop quantitative techniques to address different aspects of the underlying structures. Observable quantities used in such techniques contain uncertainty, often substantial due to the schemes in which biological data are collected. If such uncertainty is not addressed properly, our understanding of nature would be limited. Statistical modelling can account for such uncertainty. For this proposal, I develop such models, focusing on two major areas: biological network structures, and so-called change-point phenomena.