Tightness of measures

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In mathematics, tightness is a concept in measure theory. The intuitive idea is that a given collection of measures does not "escape to infinity."


Let (X, T) be a topological space, and let \Sigma be a σ-algebra on X that contains the topology T. (Thus, every open subset of X is a measurable set and \Sigma is at least as fine as the Borel σ-algebra on X.) Let M be a collection of (possibly signed or complex) measures defined on \Sigma. The collection M is called tight (or sometimes uniformly tight) if, for any \varepsilon > 0, there is a compact subset K_{\varepsilon} of X such that, for all measures \mu \in M,

|\mu| (X \setminus K_{\varepsilon}) < \varepsilon.

where |\mu| is the total variation measure of \mu. Very often, the measures in question are probability measures, so the last part can be written as

\mu (K_{\varepsilon}) > 1 - \varepsilon. \,

If a tight collection M consists of a single measure \mu, then (depending upon the author) \mu may either be said to be a tight measure or to be an inner regular measure.

If Y is an X-valued random variable whose probability distribution on X is a tight measure then Y is said to be a separable random variable or a Radon random variable.


Compact spaces[edit]

If X is a metrisable compact space, then every collection of (possibly complex) measures on X is tight. This is not necessarily so for non-metrisable compact spaces. If we take [0,\omega_1] with its order topology, then there exists a measure \mu on it that is not inner regular. Therefore the singleton \{\mu\} is not tight.

Polish spaces[edit]

If X is a Polish space, then every probability measure on X is tight. Furthermore, by Prokhorov's theorem, a collection of probability measures on X is tight if and only if it is precompact in the topology of weak convergence.

A collection of point masses[edit]

Consider the real line \mathbb{R} with its usual Borel topology. Let \delta_{x} denote the Dirac measure, a unit mass at the point x in \mathbb{R}. The collection

M_{1} := \{ \delta_{n} | n \in \mathbb{N} \}

is not tight, since the compact subsets of \mathbb{R} are precisely the closed and bounded subsets, and any such set, since it is bounded, has \delta_{n}-measure zero for large enough n. On the other hand, the collection

M_{2} := \{ \delta_{1 / n} | n \in \mathbb{N} \}

is tight: the compact interval [0, 1] will work as K_{\varepsilon} for any \varepsilon > 0. In general, a collection of Dirac delta measures on \mathbb{R}^{n} is tight if, and only if, the collection of their supports is bounded.

A collection of Gaussian measures[edit]

Consider n-dimensional Euclidean space \mathbb{R}^{n} with its usual Borel topology and σ-algebra. Consider a collection of Gaussian measures

\Gamma = \{ \gamma_{i} | i \in I \},

where the measure \gamma_{i} has expected value (mean) m_{i} \in \mathbb{R}^{n} and covariance matrix C_{i} \in \mathbb{R}^{n \times n}. Then the collection \Gamma is tight if, and only if, the collections \{ m_{i} | i \in I \} \subseteq \mathbb{R}^{n} and \{ C_{i} | i \in I \} \subseteq \mathbb{R}^{n \times n} are both bounded.

Tightness and convergence[edit]

Tightness is often a necessary criterion for proving the weak convergence of a sequence of probability measures, especially when the measure space has infinite dimension. See

Exponential tightness[edit]

A generalization of tightness is the concept of exponential tightness, which has applications in large deviations theory. A family of probability measures (\mu_{\delta})_{\delta > 0} on a Hausdorff topological space X is said to be exponentially tight if, for any \varepsilon > 0, there is a compact subset K_{\varepsilon} of X such that

\limsup_{\delta \downarrow 0} \delta \log \mu_{\delta} (X \setminus K_{\varepsilon}) < - \varepsilon.


  • Billingsley, Patrick (1995). Probability and Measure. New York, NY: John Wiley & Sons, Inc. ISBN 0-471-00710-2. 
  • Billingsley, Patrick (1999). Convergence of Probability Measures. New York, NY: John Wiley & Sons, Inc. ISBN 0-471-19745-9. 
  • Ledoux, Michel; Talagrand, Michel (1991). Probability in Banach spaces. Berlin: Springer-Verlag. pp. xii+480. ISBN 3-540-52013-9.  MR 1102015 (See chapter 2)