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Analyzing Complex Stochastic Models using Approximate Bayesian Computation

使用近似贝叶斯计算分析复杂随机模型

基本信息

批准号：
8537205
负责人：
Brandon Turner
金额：
$ 5.22万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-09-24 至 2014-09-22
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): There are a number of theoretical and practical reasons to adopt the Bayesian framework over classical techniques. Recently, many psychological models have benefited from Bayesian analyses, and in particular, they have benefited from hierarchical analyses that provide information on multiple levels. However, the class of models that are capable of enjoying the benefits of Bayesian analyses has been limited to models that possess tractable likelihood functions. These models are typically referred to as "simulation-based" models, and as a result of their complicated or intractable likelihood functions, Bayesian analyses are not possible. However, a new technique, called approximate Bayesian computation (ABC), allows researchers to circumvent the evaluation of the likelihood by simulating the model. Our proposed research will extend the application of ABC to complex, stochastic models of computational neuroscience. For these models, we will be interested in fitting hierarchical and finite mixture versions of the models to examine individual differences and explore the role of experimental design optimization in model selection.

描述（由申请人提供）：采用贝叶斯框架而不是经典技术有许多理论和实践原因。最近，许多心理学模型都受益于贝叶斯分析，特别是，它们受益于提供多层次信息的层次分析。然而，一类模型，能够享受贝叶斯分析的好处已被限制到模型，具有易处理的似然函数。这些模型通常被称为“基于模拟的”模型，由于其复杂或棘手的似然函数，贝叶斯分析是不可能的。然而，一种称为近似贝叶斯计算（ABC）的新技术允许研究人员通过模拟模型来规避可能性的评估。我们提出的研究将ABC的应用扩展到复杂的，随机的计算神经科学模型。对于这些模型，我们将有兴趣拟合模型的分层和有限混合版本，以检查个体差异并探索实验设计优化在模型选择中的作用。

项目成果

期刊论文数量（7）

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 Bayesian framework for simultaneously modeling neural and behavioral data.

DOI：
10.1016/j.neuroimage.2013.01.048
发表时间：
2013-05-15
期刊：
NEUROIMAGE
影响因子：
5.7
作者：
Turner, Brandon M.;Forstmann, Birte U.;Wagenmakers, Eric-Jan;Brown, Scott D.;Sederberg, Per B.;Steyvers, Mark
通讯作者：
Steyvers, Mark

Likelihood-free Bayesian analysis of memory models.

记忆模型的无似然贝叶斯分析。