Advances for Bayesian Model Selection and Inference

贝叶斯模型选择和推理的进展

• 批准号: 1406563
• 负责人: Edward George
• 金额: $ 45万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1406563&HistoricalAwards=false
• 关键词: Advances; Bayesian; Model; Selection; Inference

This project will develop new approaches that will unleash the potential of Bayesian methods for extracting meaningful structure in the "big data" that are of increasing interest and importance today. This will entail the creation of rapidly computable methods that enhance or bypass the much slower simulation methods upon which Bayesian methods have come to rely. By more efficiently harnessing the power of high-performance computing, our methods will provide a feasible vehicle for uncovering systematic associations in data across a variety of scientific disciplines. Examples include (a) genomics, where identifying the genetic determinants of diseases can substantially enhance targeted therapeutic decisions, (b) neuroimaging, where efficient methods will greatly facilitate understanding the complex brain architecture, and (c) environmental sciences, where identifying the determinants of climate change can better inform policy decisions. The main thrust of this research will be the development of a broad framework for fast deterministic search implementations for Bayesian model selection and inference. Building on new directions recently opened up by EMVS, a fast new alternative to stochastic search, this work will vastly increase the feasibility of general Bayesian implementations for large high-dimensional problems. This will entail the development of algorithms for classes of models that go beyond the canonical normal linear model. The speed of these algorithms will enable the broad application of new dynamic posterior exploration. Properties of the implicit selective shrinkage selection will be studied and asymptotic theory developed. New approaches to posterior reconstruction will provide new avenues for inference. Effectively, this project will lay the groundwork for a new paradigm for posterior exploration in large high-dimensional problems.

该项目将开发新的方法，这些方法将释放贝叶斯方法在当今越来越重要的“大数据”中提取有意义的结构的潜力。 这将需要创建快速计算的方法，从而增强或绕过贝叶斯方法依赖的较慢的仿真方法。 通过更有效地利用高性能计算的力量，我们的方法将为揭示各种科学学科的数据中的系统关联提供可行的工具。示例包括（a）基因组学，其中识别疾病的遗传决定因素可以实质上增强有针对性的治疗决策，（b）神经影像学，有效的方法将极大地促进复杂的大脑结构，以及（c）环境科学，在这里确定气候变化的确定性可以更好地促进政策决策。 这项研究的主要目的是开发用于贝叶斯模型选择和推理的快速确定性搜索实现的广泛框架。 这项工作将在新的方向上建立在新方向上，这是一种快速的随机搜索替代方案，这项工作将大大提高贝叶斯实施对大型高维问题的可行性。这将需要开发超出规范正常线性模型的模型类别的算法。这些算法的速度将实现新的动态后探测的广泛应用。将研究隐式选择性收缩选择的特性，并开发渐近理论。后验重建的新方法将为推理提供新的途径。 有效地，该项目将为大型高维问题的后探测带来新的范式。