Developing New Algebraic Geometric Information Criteria for Monte Carlo Inference and Model Selection in Latent Variable and Missing Data Problems

统计模型的选择是一个极具挑战性的问题。描述标准模型选择问题的一种方式是，人们希望选择一种方法来很好地符合观测数据，同时最小化模型的复杂性(以避免过度拟合观测数据和失去对其他情况的模型的泛化)。实际中使用了许多标准的模型选择准则，如Akaike的信息准则(AIC)、贝叶斯信息准则(BIC)或最小描述长度(MDL)以及偏差信息准则(DIC)。然而，在许多情况下，由于统计模型和/或数据的复杂性(例如，在存在缺失数据或测量误差的情况下)，用于选择模型的标准是无效的，甚至可能是不可计算的。最近提出的一种机器学习方法使用代数几何中的奇点分解方法来解决复杂统计模型中的这些问题。

Statistical model selection is an extremely challenging problem. One way to characterize the standard model selection problem is that one wants to choose a method that fits the observed data well while minimizing the complexity of the model (in order to avoid overfitting the observed data and losing generalization of the model to other situations). Many standard model selection criteria are used in practice, such as Akaike's Information Criterion (AIC), the Bayesian Information Criterion (BIC) or Minimum Description Length (MDL), and the Deviance Information Criterion (DIC). However, in many contexts, the criteria used to choose models are not valid or may not be even be calculable because of the complexity of the statistical model and/or data (e.g. in the presence of missing data or measurement error). A recently proposed approach in machine learning uses the resolution of singularities method from algebraic geometry to address these issues in complex statistical models.