2008 Workshop on Bayesian Model Selection and Objective Methods

2008年贝叶斯模型选择和客观方法研讨会

• 批准号: 0742079
• 负责人: Hani Doss
• 金额: $ 1万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-12-01 至 2008-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0742079&HistoricalAwards=false
• 关键词: 2008; Workshop; Bayesian; Model; Selection

Model selection in the frequentist setting is a well developed field. In the Bayesian framework, in principle, model selection is very simple. Prior probability distributions are used to describe the uncertainty surrounding all unknowns, including models being considered and the parameters for these models. After observing the data, the posterior distribution provides a coherent post data summary of the remaining uncertainty which is relevant for model selection. However, the practical implementation of this approach is not straightforward, and involves issues such as choice of priors, interpretability, and computational feasibility. In this workshop, twelve distinguished individuals who work in Bayesian model selection present their work in a number of different areas, including determination of good objective priors, assessment of various information criteria (AIC, BIC, RIC), methods of calculation of Bayes factors, and Markov chain Monte Carlo. A number of young researchers participate in the workshop and present their work in poster sessions.Variable selection is an important and pervasive problem in scientific and medical research. A few important variables are to be selected from many candidates and used for understanding, prediction and decision making. Historically, variable selection has been carried out in a frequentist setting. However, Bayesian approaches offer important advantages. In broad terms, they give a coherent way of dealing with the distribution of the future response of an individual for whom the predictor variables are now known. Recent advances in both computing power and statistical methodology have greatly enhanced the feasibility of Bayesian approaches to regression and variable selection. The workshop provides an excellent opportunity to discuss the many recent significant developments in Bayesian model selection and objective methods; to discuss what has been found to work and what does not; and to identify important problems and new research directions.

频域环境下的模型选择是一个发展很好的领域。在贝叶斯框架中，原则上，模型选择非常简单。先验概率分布用于描述围绕所有未知数的不确定性，包括正在考虑的模型和这些模型的参数。在观察数据之后，后验分布提供了与模型选择相关的剩余不确定性的一致后验数据汇总。然而，这种方法的实际实现并不简单，而且涉及到诸如先验选择、可解释性和计算可行性等问题。在这次研讨会上，12位致力于贝叶斯模型选择的杰出人士介绍了他们在许多不同领域的工作，包括良好客观先验的确定，各种信息标准(AIC，BIC，RIC)的评估，贝叶斯因子的计算方法，以及马尔可夫链蒙特卡罗。一些年轻的研究人员参加了研讨会，并在海报上展示了他们的工作。变量选择是科学和医学研究中一个重要而普遍的问题。一些重要的变量将从许多候选人中挑选出来，用于理解、预测和决策。在历史上，变量选择一直是在频率设置中进行的。然而，贝叶斯方法提供了重要的优势。广义地说，它们给出了一种连贯的方法来处理预测变量现在已知的个人未来反应的分布。最近在计算能力和统计方法方面的进步极大地提高了贝叶斯方法用于回归和变量选择的可行性。讲习班提供了一个极好的机会，可以讨论贝叶斯模型选择和客观方法方面的许多最新重大发展；讨论已发现可行和不可行的办法；并确定重要问题和新的研究方向。