Comparing families of dynamic causal models.
Comparing families of dynamic causal models.
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DOI：
10.1371/journal.pcbi.1000709
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发表时间：
2010-03-12
影响因子：
4.3
通讯作者：
Leff AP
中科院分区：
文献类型：
--
作者：
Penny WD;Stephan KE;Daunizeau J;Rosa MJ;Friston KJ;Schofield TM;Leff AP
科学数据的数学模型可以使用贝叶斯模型证据进行正式比较。以前在生物科学中的应用主要集中在模型选择上，其中首先选择具有最高证据的模型，然后基于该模型的参数进行推断。这种“最佳模型”方法非常有用，但如果有大量模型要比较，并且如果不同的主体使用不同的模型，则可能变得脆弱。为了克服这一缺点，我们提出了两种进一步的方法相结合：（i）家庭水平的推断和（ii）贝叶斯模型平均家庭内。族级推理消除了模型结构方面的不确定性，而不是感兴趣的特征。例如：系统的输入是什么？是串行处理还是并行处理？它是线性的还是非线性的？它是由一个单一的，关键的连接介导的吗？我们应用贝叶斯模型平均家庭内提供的参数，是独立的模型结构的进一步假设的推断。我们说明的方法使用动态因果模型的脑成像数据。贝叶斯模型比较为评估生物科学中不同的计算模型提供了一种形式化的方法。新兴的应用领域包括基于微分方程的神经元和生物化学网络的动力学模型。在这一领域以前的许多工作都集中在选择单一的最佳模型。这种方法是有用的，但如果有大量的模型进行比较，如果不同的主题使用不同的模型，可能会变得脆弱。本文表明，这些问题可以克服与使用家庭级推理和贝叶斯模型平均模型家庭。
Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This “best model” approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data. Bayesian model comparison provides a formal method for evaluating different computational models in the biological sciences. Emerging application domains include dynamical models of neuronal and biochemical networks based on differential equations. Much previous work in this area has focussed on selecting the single best model. This approach is useful but can become brittle if there are a large number of models to compare and if different subjects use different models. This paper shows that these problems can be overcome with the use of Family Level Inference and Bayesian Model Averaging within model families.
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