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

用于建模生物网络结构的潜在因子纵向社交网络和贝叶斯融合，以及用于建模自然现象的纵向/空间弯曲电缆

• 批准号: 261806-2008
• 负责人: Chiu, Grace
• 金额: $ 1.02万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=395244
• 关键词: Latent; factor; longitudinal; social; networks

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.

许多自然系统和现象是非常复杂的。为了充分了解它们，科学家们开发了定量技术来解决底层结构的不同方面。这些技术中使用的可观测量含有不确定性，由于收集生物数据的方法，这些不确定性往往很大。如果这种不确定性得不到妥善解决，我们对自然的理解就会受到限制。统计模型可以解释这种不确定性。对于这个建议，我开发这样的模型，集中在两个主要领域：生物网络结构，和所谓的变点现象。