Marginal likelihood based Bayesian model selection

基于边际似然的贝叶斯模型选择

• 批准号: 356000-2008
• 负责人: Lefebvre, Geneviève
• 金额: $ 0.87万
• 依托单位: Université du Québec à Montréal
• 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=497290
• 关键词: Marginal; likelihood; based; Bayesian; model

In this research proposal we consider some aspects of the marginal likelihood as a criterion for Bayesian model selection. The marginal likelihood is defined as the marginal density of the data under a given model, and can be interpreted as a measure of how the data supports the model. The higher this value, the more likely is the model under consideration. Hence, a general rule is to select the model yielding the maximal marginal likelihood value. When we wish to compare two models, the decision can be based on the value of the Bayes factor, which is defined as the ratio of the respective marginal likelihoods.

在这项研究建议中，我们将边际可能性的某些方面视为贝叶斯模型选择的标准。边际可能性定义为在给定模型下数据的边际密度，并且可以解释为数据如何支持该模型的方法。该值越高，正在考虑的模型的可能性就越大。因此，一般规则是选择产生最大边缘可能性值的模型。当我们希望比较两个模型时，该决定可以基于贝叶斯因子的值，贝叶斯因子的价值定义为各个边际似然的比率。