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: 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=402811
• 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.

在这项研究中，我们考虑一些方面的边际似然作为贝叶斯模型选择的标准。边际似然被定义为给定模型下数据的边际密度，并且可以被解释为数据如何支持模型的度量。该值越高，所考虑的模型越可能。因此，一般规则是选择产生最大边际似然值的模型。当我们希望比较两个模型时，可以基于贝叶斯因子的值进行决策，贝叶斯因子被定义为各自的边际似然比。