In mathematics — specifically, in ergodic theory — a maximising measure is a particular kind of probability measure. Informally, a probability measure μ is a maximising measure for some function f if the integral of f with respect to μ is "as big as it can be". The theory of maximising measures is relatively young and quite little is known about their general structure and properties.

## Definition

Let X be a topological space and let T : X → X be a continuous function. Let Inv(T) denote the set of all Borel probability measures on X that are invariant under T, i.e., for every Borel-measurable subset A of X, μ(T−1(A)) = μ(A). (Note that, by the Krylov-Bogolyubov theorem, if X is compact and metrizable, Inv(T) is non-empty.) Define, for continuous functions f : X → R, the maximum integral function β by

${\displaystyle \beta (f):=\sup \left.\left\{\int _{X}f\,\mathrm {d} \nu \right|\nu \in \mathrm {Inv} (T)\right\}.}$

A probability measure μ in Inv(T) is said to be a maximising measure for f if

${\displaystyle \int _{X}f\,\mathrm {d} \mu =\beta (f).}$

## References

• Morris, Ian (2006). Topics in Thermodynamic Formalism: Random Equilibrium States and Ergodic Optimisation (Ph.D. thesis). University of Manchester. ProQuest 2115076468.
• Jenkinson, Oliver (2006). "Ergodic optimization". Discrete and Continuous Dynamical Systems. 15 (1): 197–224. doi:10.3934/dcds.2006.15.197. ISSN 1078-0947. MR2191393