[Math] Vague convergence of measures

measure-theoryprobability theory

Define a subprobability measure to be a measure on Borel sigma algebra of the real line $\mathbb{R}$ with the measure for the whole real line less or equal than 1.

I was wondering about the definition of vague convergence of a sequence of subprobability measures $\{ \mu_n, n\geq 1 \}$ to another subprobability measure $\mu$. The convergence can be defined in slightly two different ways as in Chung's probability theory book p85 and p90:

(1) if there exists a dense subset D of the real line $\mathbb{R}$ so that $ \forall a \text{ and } b \in D \text{ with } a <b, \mu_n((a,b]) \rightarrow \mu((a,b])$.

(2) if there exists a dense subset D of the real line $\mathbb{R}$ so that $ \forall a \text{ and } b \in D \text{ with } a <b, \mu_n((a,b)) \rightarrow \mu((a,b))$ (the original text just says converges, not mention that converges to $ \mu((a,b))$ which I guess can be added?).

How to show these two definitions are equivalent?

A side question:
is there a definition of vague convergence for general measures on more general sigma algebra with more general underlying space?

Thank you so much!

Best Answer

Here is the general notion of vague convergence, taken from Olav Kallenberg's Foundations of Modern Probability (2nd edition).

Let $S$ be a locally compact, second countable Hausdorff space equipped with its Borel $\sigma$-field ${\cal S}$. Let $\hat{\cal S}$ be the ring of relatively compact Borel sets, and ${\cal M}(S)$ the space of locally finite non-negative measures. Locally finite means that $\mu(B)<\infty$ when $B\in \hat{\cal S}$.

The vague topology on ${\cal M}(S)$ is the topology generated by the mappings $\mu\mapsto \int f\ d\mu$ for every $f$ a non-negative continuous function with compact support.

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