In the previous post, we saw that it isn’t much extra effort to define the DGFF with non-zero boundary conditions, by adding onto the zero-BC DGFF the unique (deterministic) harmonic function which extends the boundary values into the domain. We also saw how a Gibbs-Markov property applies, whereby the values taken by the field on some sub-region depend on the values taken on only through values taken on .
In this post, we look at how this property and some other methods are applied by Deuschel  to study the probability that the DGFF on a large box in is positive ‘everywhere’. This event can be interpreted in a couple of ways, all of which are referred to there as entropic repulsion. Everything which follows is either taken directly or paraphrased directly from . I have tried to phrase this in a way which avoids repeating most of the calculations, instead focusing on the methods and the motivation for using them.
Fix dimension throughout. We let be the law of the DGFF on with zero boundary conditions. Then for any subset , in an intuitively-clear abuse of notation, we let
be the event that some random field h takes only non-negative values on A. The goal is to determine . But for the purposes of this post, we will focus on showing bounds on the probability that the field is non-negative on a thin annulus near the boundary of , since this is a self-contained step in the argument which contains a blog-friendly number of ideas.
We set to be a sequence of integers greater than one (to avoid dividing by zero in the statement), for which . We now define for each N, the annulus
with radius set a distance inside the box . We aim to control . This forms middle steps of Deuschel’s Propositions 2.5 and 2.9, which discuss . Clearly there is the upper bound
and a lower bound on is obtained in the second proposition by considering the box as a union of annuli then combining the bounds on each annulus using the FKG inequality.
Upper bound via odds and evens
After removing step (1), this is Proposition 2.5:
This is giving a limiting upper bound on the probability of the form , though as with all LDP estimates, the form given at (2) is more instructive.
Morally, the reason why it is unlikely that the field should be non-negative everywhere within the annulus is that the distribution at each location is centred, and even though any pair of values are positively correlated, this correlation is not strong enough to avoid this event being unlikely. But this is hard to corral into an upper bound argument directly. In many circumstances, we want to prove upper bounds for complicated multivariate systems by projecting to get an unlikely event for a one-dimensional random variable, or a family of independent variables, even if we have to throw away some probability. We have plenty of tools for tail probabilities in both of these settings. Since the DGFF is normal, a one-dimensional RV that is a linear combination (eg the sum) of all the field heights is a natural candidate. But in this case we would have thrown away too much probability, since the only way we could dominate is to demand that the sum , which obviously has probability 1/2 by symmetry. (3)
So Deuschel splits into , where the former includes all vertices with odd total parity in and the latter includes all the vertices with even total parity in the interior of . (Recall that is bipartite in exactly this fashion). The idea is to condition on . But obviously each even vertex is exactly surrounded by odd vertices. So by the Gibbs-Markov property, conditional on the odd vertices, the values of the field at the even vertices are independent. Indeed, if for each we define to be the average of its neighbours (which is measurable w.r.t to the sigma-algebra generated by the odd vertices), then
is a collection of independent normals with variance one, and where the mean of is .
To start finding bounds, we fix some threshold to be determined later, and consider the odd-measurable event that at most half of the even vertices v have . So says that all the odd vertices are non-negative and many are quite large. This certainly feels like a low-probability event, and unlike at (3), we might be able to obtain good tail bounds by projection into one dimension.
In the other case, conditional on , there are a large number of even vertices with conditional mean at most m, and so we can control the probability that at least one is negative as a product
Note that for this upper bound, we can completely ignore the other even vertices (those with conditional mean greater than m).
So we’ll go back to . For computations, the easiest one-dimensional variable to work with is probably the mean of the s across , since on this is at least . Rather than focus on the calculations themselves involving
let us remark that it is certainly normal and centered, and so there are many methods to bound its tail, for example
as used by Deuschel just follows from an easy comparison argument within the integral of the pdf. We can tackle the variance using the Green’s function for the random walk (recall the first post in this set). But before that, it’s worth making an observation which is general and useful, namely that is the expectation of
conditional on the odds. Directly from the law of total variance, the variance of any random variable X is always larger than the variance of .
So in this case, we can replace in (5) with , which can be controlled via the Green’s function calculation.
Finally, we choose so that the probability at (4) matches the probability at (5) in scale, and this choice leads directly to (2).
In summary, we decomposed the event that everything is non-negative into two parts: either there are lots of unlikely local events in the field between an even vertex and its odd neighbours, or the field has to be atypically large at the odd sites. Tuning the parameter allows us to control both of these probabilities in the sense required.
Lower bound via a sparse sub-lattice
To get a lower bound on the probability that the field is non-negative on the annulus, we need to exploit the positive correlations in the field. We use a similar idea to the upper bound. If we know the field is positive and fairly large in many places, then it is increasingly likely that it is positive everywhere. The question is how many places to choose?
We are going to consider a sub-lattice that lives in a slightly larger region than itself, and condition the field to be larger than everywhere on this lattice. We want the lattice to be sparse enough that even if we ignore positive correlations, the chance of this happening is not too small. But we also want the lattice to be dense enough that, conditional on this event, the chance that the field is actually non-negative everywhere in is not too small either.
To achieve this, Deuschel chooses a sub-lattice of width , and sets to be the intersection of this with the annulus with radii , to ensure it lives in a slightly larger region than itself. The scaling of this sub-lattice density is such that when a random walk is started at any , the probability that the RW hits before is asymptotically in (0,1). (Ie, not asymptotically zero or one – this requires some definitely non-trivial calculations.) In particular, for appropriate (ie large enough) choice of , this probability is at least 1/2 for all . This means that after conditioning on event , the conditional expectation of is at least for all . Again this uses the Gibbs-Markov property and the Gaussian nature of the field. In particular, this conditioning means we are left with the DGFF on , ie with boundary , and then by linearity, the mean at non-boundary points is given by the harmonic extension, which is linear (and so increasing) in the boundary values.
At this point, the route through the calculations is fairly clear. Since we are aiming for a lower bound on the probability of the event , it’s enough to find a lower bound on .
Now, by positive correlation (or, formally, the FKG inequality) we can control just as a product of the probabilities that the field exceeds the threshold at each individual site in . Since the value of the field at each site is normal with variance at least 1 (by definition), this is straightforward.
Finally, we treat . We’ve established that, conditional on B, the mean at each point of is at least , and we can bound the variance above too. Again, this is a conditional variance, and so is at most the corresponding original variance, which is bounded above by . (This fact that the variance is maximised at the centre is intuitively clear when phrased in terms of occupation times, but the proof is non-obvious, or at least non-obvious to me.)
Since each of the event for is positively correlated with B, we can bound the probability it holds for all v by the product of the probabilities that it holds for each v. But having established that the conditional mean is at least for each v, and the variance is uniformly bounded above (including in N), this gives an easy tail bound of the form we require.
Again it just remains to choose the sequence of thresholds to maximise the lower bound on the probability that we’ve found in this way. In both cases, it turns out that taking is sensible, and this turns out to be linked to the scaling of the maximum of the DGFF, which we will explore in the future.
 – J-D Deuschel, Entropic Repulsion of the Lattice Free Field, II. The 0-Boundary Case. Available at ProjectEuclid.