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PERCOLATION AND DISORDERED SYSTEMS Geoffrey GRIMMETT

PERCOLATION AND DISORDERED SYSTEMS Geoffrey GRIMMETT

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170 <strong>Geoffrey</strong> Grimmett<br />

Theorem 5.5 (Holley’s Inequality). Let µ1 and µ2 be positive probability<br />

measures on (ΩE, FE) such that<br />

(5.6) µ1(ω1 ∨ ω2)µ2(ω1 ∧ ω2) ≥ µ1(ω1)µ2(ω2) for all ω1, ω2 ∈ ΩE.<br />

Then<br />

µ1(f) ≥ µ2(f) for all increasing f : ΩE → R,<br />

which is to say that µ1 ≥ µ2.<br />

Proof of Theorem 5.5. The theorem is ‘merely’ a numerical inequality involving<br />

a finite number of positive reals. It may be proved in a totally elementary<br />

manner, using essentially no general mechanism. Nevertheless, in a more<br />

useful (and remarkable) proof we construct Markov chains and appeal to the<br />

ergodic theorem. This requires a mechanism, but the method is beautiful,<br />

and in addition yields a structure which finds applications elsewhere.<br />

The main step is the proof that µ1 and µ2 can be ‘coupled’ in such a<br />

way that the component with marginal measure µ1 lies above (in the sense<br />

of sample realisations) that with marginal measure µ2. This is achieved by<br />

constructing a certain Markov chain with the coupled measure as unique<br />

invariant measure.<br />

Here is a preliminary calculation. Let µ be a positive probability measure<br />

on (ΩE, FE). We may construct a time-reversible Markov chain with state<br />

space ΩE and unique invariant measure µ, in the following way. We do this by<br />

choosing a suitable generator (or ‘Q-matrix’) satisfying the detailed balance<br />

equations. The dynamics of the chain involve the ‘switching on or off’ of<br />

components of the current state. For ω ∈ ΩE, let ωe and ωe be given as in<br />

(4.1). Define the function G : Ω2 E → R by<br />

(5.7) G(ωe, ω e ) = 1, G(ω e , ωe) = µ(ωe)<br />

µ(ω e ) ,<br />

for all ω ∈ ΩE, e ∈ E; define G(ω, ω ′ ) = 0 for all other pairs ω, ω ′ with<br />

ω �= ω ′ . The diagonal elements are chosen so that<br />

It is elementary that<br />

�<br />

G(ω, ω ′ ) = 0 for all ω ∈ ΩE.<br />

ω ′<br />

µ(ω)G(ω, ω ′ ) = µ(ω ′ )G(ω ′ , ω) for all ω, ω ′ ∈ ΩE,<br />

and therefore G generates a time-reversible Markov chain on the state space<br />

ΩE. This chain is irreducible (using (5.7)), and therefore has a unique invariant<br />

measure µ (see [169], p. 208).

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