Collaborative Research: Bayesian Inference for Interpretable Random Structures
合作研究:可解释随机结构的贝叶斯推理
基本信息
- 批准号:1952679
- 负责人:
- 金额:$ 14.99万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-05-01 至 2024-04-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Many challenging questions in data science can be characterized in terms of inference for random subsets of patients, customers, proteins, symptoms, or other experimental units. Examples include the search for a subpopulation of patients who most benefit from a given treatment, the identification of subsets of mutations that characterize different tumor cell subpopulations that could then serve as possible treatment targets, or the discovery of latent disease patterns in electronic health records that could be used to propose an improved allocation of resources. In all three examples, the unusual nature of the inference targets as random subsets gives rise to challenging data analysis problems. In contrast, most traditional methods work for inference targets that are a single number, like a treatment effect, a level of differential protein expression, or a mean response. This project aims to address this gap in currently available methodology by developing and applying new methods to solve several specific inference problems related to random subsets.This project develops novel statistical inference methods for random subsets to approach such inference problems by explicitly introducing parsimony and interpretability as criteria for the reported inference. Related methods are developed for random partitions, feature allocation, and extensions of such structures. Besides the development of models and inference paradigms, a second major thrust of the proposed work is the development of computationally feasible implementations for large data sets. Model-based Bayesian inference for random subsets quickly leads to prohibitively computation-intensive implementations when simulation-exact posterior Monte Carlo methods are used. While several big data posterior simulation methods for global parameters have been developed in recent literature, there are few such methods for random subsets, i.e., local parameters. The project will explore several approaches to develop such methods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
数据科学中的许多挑战性问题都可以通过对患者、客户、蛋白质、症状或其他实验单位的随机子集进行推断来表征。例如,搜索从给定治疗中获益最多的患者亚群,识别表征不同肿瘤细胞亚群的突变子集,然后可以作为可能的治疗靶点,或者发现电子健康记录中的潜在疾病模式,可以用于提出改进的资源分配。 在所有三个例子中,推理目标作为随机子集的不寻常性质引起了具有挑战性的数据分析问题。相比之下,大多数传统方法适用于单一数字的推断目标,如治疗效果,差异蛋白质表达水平或平均反应。 本项目旨在通过开发和应用新的方法来解决与随机子集相关的几个特定推理问题,从而弥补目前可用方法中的这一差距。本项目通过明确引入简约性和可解释性作为报告推理的标准,为随机子集开发新的统计推理方法,以解决此类推理问题。相关的方法开发的随机分区,功能分配,并扩展这样的结构。除了模型和推理范式的发展,拟议的工作的第二个主要推力是计算上可行的实现大型数据集的发展。当使用模拟精确后验蒙特卡罗方法时,随机子集的基于模型的贝叶斯推理很快导致计算密集型的实现。虽然在最近的文献中已经开发了几种用于全局参数的大数据后验模拟方法,但是很少有用于随机子集的这样的方法,即,局部参数这个奖项反映了NSF的法定使命,并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。
项目成果
期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A Comparative Tutorial of Bayesian Sequential Design and Reinforcement Learning
- DOI:10.1080/00031305.2022.2129787
- 发表时间:2022-05
- 期刊:
- 影响因子:0
- 作者:Mauricio Tec;Yunshan Duan;P. Müller
- 通讯作者:Mauricio Tec;Yunshan Duan;P. Müller
Clustering and Prediction With Variable Dimension Covariates
具有可变维度协变量的聚类和预测
- DOI:10.1080/10618600.2021.1999824
- 发表时间:2021
- 期刊:
- 影响因子:2.4
- 作者:Page, Garritt L.;Quintana, Fernando A.;Müller, Peter
- 通讯作者:Müller, Peter
Bayesian Nonparametric Bivariate Survival Regression for Current Status Data
当前状态数据的贝叶斯非参数双变量生存回归
- DOI:10.1214/22-ba1346
- 发表时间:2022
- 期刊:
- 影响因子:4.4
- 作者:Paulon, Giorgio;Müller, Peter;Sal y Rosas, Victor G.
- 通讯作者:Sal y Rosas, Victor G.
A semiparametric Bayesian approach to population finding with time‐to‐event and toxicity data in a randomized clinical trial
使用半参数贝叶斯方法在随机临床试验中使用事件发生时间和毒性数据进行群体发现
- DOI:10.1111/biom.13289
- 发表时间:2021
- 期刊:
- 影响因子:1.9
- 作者:Morita, Satoshi;Müller, Peter;Abe, Hiroyasu
- 通讯作者:Abe, Hiroyasu
The Dependent Dirichlet Process and Related Models
- DOI:10.1214/20-sts819
- 发表时间:2022-02-01
- 期刊:
- 影响因子:5.7
- 作者:Quintana,Fernando A.;Muller,Peter;MacEachern,Steven N.
- 通讯作者:MacEachern,Steven N.
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Peter Mueller其他文献
A Unified Decision Framework for Phase I Dose-Finding Designs
I 期剂量探索设计的统一决策框架
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:1
- 作者:
Yunshan Duan;Shijie Yuan;Yuan Ji;Peter Mueller - 通讯作者:
Peter Mueller
704 PERCUTANEOUS RENAL MASS BIOPSY: IF THEY'RE POSITIVE, THEY'RE POSITIVE, BUT IF THEY'RE NEGATIVE, BE CAREFUL! A CORRELATION BETWEEN RENAL BIOPSY AND SURGICAL PATHOLOGY
- DOI:
10.1016/j.juro.2011.02.1671 - 发表时间:
2011-04-01 - 期刊:
- 影响因子:
- 作者:
Sameer M. Deshmukh;Luiz Sequeira;Francis McGovern;Douglas Dahl;Aria Olumi;Brian Eisner;W. Scott McDougal;Peter Mueller;Anthony Samir;Adam S Feldman - 通讯作者:
Adam S Feldman
Randomized, Double-Blind, Placebo-Controlled, Phase III Study of Recombinant Human Granulocyte-Macrophage Colony-Stimulating Factor as Adjunct to Induction Treatment of High-Grade Malignant Non-Hodgkin's Lymphomas
- DOI:
10.1182/blood.v82.8.2329.2329 - 发表时间:
1993-10-15 - 期刊:
- 影响因子:
- 作者:
Heinrich H. Gerhartz;Marianne Engelhard;Peter Meusers;Gunter Brittinger;Wolfgang Wilmanns;Gunther Schlimok;Peter Mueller;Dieter Huhn;Reinhard Musch;Wolfgang Siegert;Diana Gerhartz;Joachim H. Hartlapp;Eckhard Thiel;Christoph Huber;Christian Peschl;Wolfgang Spann;Bertold Emmerich;Christine Schadek;Martin Westerhausen;Hans-Wilhelm Pees - 通讯作者:
Hans-Wilhelm Pees
Regression with Variable Dimension Covariates
具有可变维度协变量的回归
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Peter Mueller;F. Quintana;G. Page - 通讯作者:
G. Page
1266 RADIOFREQUENCY ABLATION OF CENTRALLY LOCATED RENAL TUMORS IS ASSOCIATED WITH INCREASED RATES OF CLAVIEN GRADE 3–5 COMPLICATIONS
- DOI:
10.1016/j.juro.2011.02.951 - 发表时间:
2011-04-01 - 期刊:
- 影响因子:
- 作者:
Sarah Psutka;Ali Daha;Francis McGovern;W. Scott McDougal;Peter Mueller;Debra Gervais;Adam Feldman - 通讯作者:
Adam Feldman
Peter Mueller的其他文献
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{{ truncateString('Peter Mueller', 18)}}的其他基金
Workshop on Objective Bayes Methodology
客观贝叶斯方法论研讨会
- 批准号:
1745746 - 财政年份:2017
- 资助金额:
$ 14.99万 - 项目类别:
Standard Grant
Travel Support for the 10th ISBA World Meeting on Bayesian Statistics
第十届 ISBA 贝叶斯统计世界会议的差旅支持
- 批准号:
1005529 - 财政年份:2010
- 资助金额:
$ 14.99万 - 项目类别:
Standard Grant
Travel support for the 9th ISBA world meeting
第九届 ISBA 世界会议的差旅支持
- 批准号:
0808859 - 财政年份:2008
- 资助金额:
$ 14.99万 - 项目类别:
Standard Grant
Fourth International Workshop on Objective Prior Methodology
第四届客观先验方法论国际研讨会
- 批准号:
0245166 - 财政年份:2003
- 资助金额:
$ 14.99万 - 项目类别:
Standard Grant
U.S.-Mexico Cooperative Research: Simulation Based Sequential Design: Species Diversity
美国-墨西哥合作研究:基于仿真的序贯设计:物种多样性
- 批准号:
0203207 - 财政年份:2002
- 资助金额:
$ 14.99万 - 项目类别:
Standard Grant
U.S.-Chile Program: Bayesian Simulation and Partially Exchangeable Binary Sequences
美国-智利项目:贝叶斯模拟和部分可交换的二进制序列
- 批准号:
0104496 - 财政年份:2001
- 资助金额:
$ 14.99万 - 项目类别:
Standard Grant
"Nonlinear Bayesian Function Estimation in Complex Models"
“复杂模型中的非线性贝叶斯函数估计”
- 批准号:
9404151 - 财政年份:1994
- 资助金额:
$ 14.99万 - 项目类别:
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Hysteretic Behavior of Precast Panel Walls
预制板墙的滞后行为
- 批准号:
8206674 - 财政年份:1982
- 资助金额:
$ 14.99万 - 项目类别:
Continuing grant
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