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Probability Measures
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Probabilistic Dataset Models


Let $X = \set{a, b}$ and $Y = \set{0, 1}$. Define $f: X \to Y$ by $f \equiv 0$.

The dataset $(a, 0)$ is consistent with $f$. So are the datasets $((a, 0), (a, 0))$ and $((a, 0), (a, 0), (a, 0))$. Unfortunately, these datasets are “bad” in the sense that we do not see the value associated with $b$. Each dataset is consistent with the (functional) relations $\set{(a, 0), (b, 0)}$ and $\set{(a, 0), (b, 1)}$.

In other words, a dataset may be incomplete. In spite of this limitation, we want to discuss an inductor's performance on consistent datasets. One route is to put a measure on the set of training sets and only consider high-measure subsets.


Let $(X, \mathcal{X} )$ and $(Y, \mathcal{Y} )$ be measurable spaces and $R$ be a relation on $X \times Y$. Let $\mu : \mathcal{X} \times \mathcal{Y} \to [0, 1]$ be a probability measure so that $\mathcal{D} = (X \times Y, \mathcal{X} \times \mathcal{Y} , \mu )$ is a probability space.

If $\mu (B) = 0$ for all $B \subset \relcomplement{R}{X \times Y}$, then we call $\mathcal{D} $ a probabilistic dataset model for $R$. In other words, $\mu $ gives zero measure to any set of points not in the relation. Equivalently, $\mu (R) = 1$; or we observe a pair in $R$ almost surely.

If $R$ is functional, then we call $\mathcal{D} $ a supervised probabilistic dataset model. In this case, since there is a functional relation between $X$ and $Y$, we call the marginal measure $\mu _X: \mathcal{X} \to [0, 1]$ the data-generating distribution or underlying distribution since $\mu (A) = \mu _X(\Set*{x \in X}{(x, y) \in A)})$. In this case we call the (functional) relation $R$ the correct labeling function. Many authors refer to a supervised probabilistic data model as the statistical learning (theory) framework.

Probable datasets

For datasets of size $n$, we use the product measure $((X \times Y)^n, (\mathcal{X} \times \mathcal{Y} )^n, \mu ^n)$. We interpret this measure as modeling independent and identically distributed elements of $R$.

For $\delta \in (0, 1)$, $\mathcal{S} \subset (X \times Y)^n$ is $1 - \delta $-probable if $\mu ^n(S) \geq 1 - \delta $. We call $\delta $ the confidence parameter. If $\delta $ is small, we interpret $\mathcal{S} $ as a set of “reasonable” datasets.

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