构建统计模型的目的是多种多样的，但通常都需要根据某些底层结构来解释可观测值（现有或将来的数据）。由于多种原因，模型不确定性也是统计数据中最具挑战性的领域之一。首先，正在考虑的可能模型数量众多。以普通的线性回归为例，在这里我们试图通过将数据与可能的协变量线性相关来解释观察到的数据。假设我们有60个可能的解释协变量，但是我们不确定哪些应该包含在回归模型中，哪些应该省略。那么，正在考虑的可能回归模型的数量为260，甚至太大而无法枚举。
Statistical models are constructed for a variety of purposes, but typically involve an effort to explain observables (existing or future data) in terms of some underlying structure. Model uncertainty is also one of the most challenging areas of statistics, for several reasons. First, the number of possible models under consideration may be overwhelming. Take ordinary linear regression for instance, where we seek to explain observed data by linearly relating the data to possible covariates. Suppose we have 60 possible explanatory covariates, but we are unsure which should be included in the regression model and which should be omitted. Then the number of possible regression models under consideration is 260, which is even too large to enumerate.