Model selection and statistical inference in mixture distributions and hidden markov (regression) models

混合分布和隐马尔可夫（回归）模型中的模型选择和统计推断

• 批准号: 386578-2010
• 负责人: Khalili, Abbas
• 金额: $ 1.24万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=508664
• 关键词: Model; selection; statistical; inference; mixture; Model; selection; statistical; inference; mixture

Mixture distributions provide a flexible and useful statistical model for many problems arising in scientific research. They are typically used to analyze data that arise from a heterogeneous population or process. When the scientific background is insufficient to determine the appropriate order of a finite mixture model in an application, the number of components or order of the mixture is subject to inference.

混合分布为科学研究中引起的许多问题提供了灵活且有用的统计模型。它们通常用于分析由异质种群或过程产生的数据。当科学背景不足以确定应用程序中有限混合模型的适当顺序时，混合物的组件或顺序数量会进行推断。