Analyzing Complex Stochastic Models using Approximate Bayesian Computation

描述（由申请人提供）：在经典技术中采用贝叶斯框架有许多理论和实践上的原因。最近，许多心理模型受益于贝叶斯分析，特别是受益于提供多层次信息的层次分析。然而，能够享受贝叶斯分析的好处的模型类别仅限于具有易于处理的似然函数的模型。这些模型通常被称为“基于模拟的”模型，由于其复杂或难以处理的似然函数，贝叶斯分析是不可能的。然而，一种新的技术，称为近似贝叶斯计算（ABC），允许研究人员通过模拟模型来绕过可能性的评估。我们建议的研究将ABC的应用扩展到计算神经科学的复杂、随机模型。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。

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.