论文标题
VARFA：有效贝叶斯学习分析的变分因子分析框架
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics
论文作者
论文摘要
我们提出了VARFA，这是一个变分推理因子分析框架，该框架将教育数据挖掘的现有因子分析模型扩展到有效地在模型估计因素中输出不确定性估计。例如，对于自适应测试方案，这种不确定性信息是有用的，如果模型对学生的技能水平估计不确定，则可以进行其他测试。产生这种不确定性信息的传统贝叶斯推理方法在计算上很昂贵，并且不扩展到大型数据集。 VARFA利用变异推理，这使得即使在非常大的数据集上也可以有效地执行贝叶斯推断。我们使用稀疏因子分析模型作为案例研究，并证明了VARFA对合成和真实数据集的功效。 VARFA也非常通用，可以应用于各种因素分析模型。
We propose VarFA, a variational inference factor analysis framework that extends existing factor analysis models for educational data mining to efficiently output uncertainty estimation in the model's estimated factors. Such uncertainty information is useful, for example, for an adaptive testing scenario, where additional tests can be administered if the model is not quite certain about a students' skill level estimation. Traditional Bayesian inference methods that produce such uncertainty information are computationally expensive and do not scale to large data sets. VarFA utilizes variational inference which makes it possible to efficiently perform Bayesian inference even on very large data sets. We use the sparse factor analysis model as a case study and demonstrate the efficacy of VarFA on both synthetic and real data sets. VarFA is also very general and can be applied to a wide array of factor analysis models.
