Moments of Two-Variable Functions and the Uniqueness of Graph Limits
Christian Borgs, Jennifer Chayes, Laszlo Lovasz
Introduction
Our interest in these integrals stems from graph theory (see next paragraph), but such integrals appear in physics, statistics, and other areas. In many respects, these integrals can be thought of as 2-variable analogues of moments of 1-variable functions, so instead of moment sequences, such -variable functions have a ”moment graph parameter” (function defined on graphs). Just like moments of a 1-variable function determine the function up to measure preserving transformations, these “moments” determine the 2-variable function up to measure preserving transformations. The exact formulation and proof of this fact is the main goal of this paper.
Our main motivation for this study comes from the theory of convergent graph sequences. Let and be two simple graphs (graphs without loops and multiple edges). Let us map the nodes of randomly into , and let denote the probability that this map preserves adjacency. For example, denotes the edge density of . In general, we call the homomorphism density or simply the density of in .
We call a sequence of simple graphs convergent, if has a limit for every simple graph . The notion of convergent graph sequences was introduced by Borgs, Chayes, Lovász, Sós and Vesztergombi , see also , and further studied in and . Lovász and Szegedy proved that every convergent graph sequence has a “limit object” in the form of a function in the sense that
for every simple graph . In this case we say that converges to . It was also shown in that for every function there is a convergent sequence of simple graphs converging to . To complete the picture, the results in this paper imply that the limit object is unique up to measure preserving transformations.
Results
For the precise statement of our results, we need some definitions. Instead of the interval $$, we consider two-variable functions on an arbitrary probability space; while this does not add real generality it leads to a cleaner picture. We need a few definitions.
We start by recalling some basic notions from probability theory. Let be a probability space (where is the underlying set, is a -algebra on , and is a probability measure on ). As usual, is called complete if contains all sets of external measure , and the completion of is obtained by replacing with the -algebra generated by and all subsets of external measure .
Let and be probability spaces, and let be a measure preserving map from to . The map is called an isomorphism if it is a bijection between and and both and are measure preserving, and it is called an isomorphism mod if there are null sets and such that the restriction of to is an isomorphism between and (equipped with the suitable restrictions of and , respectively). In the last case and are called isomorphic mod .
It turns out that several of our results require a little bit more structure than that of an arbitrary probability space. In particular, we will consider Lebesgue spaces, i.e., complete probability spaces that are isomorphic mod to the disjoint union of a closed interval (equipped with the standard Lebesgue sets and Lebesgue measure) and a countable set of atoms. See , Section 2.2 for an axiomatic definition of Lebesgue spaces, and Section 2.4 for the proof that a probability space is Lebesgue if and only if it is isomorphic mod 0 to the disjoint union of a closed interval and a countable set of atoms.
We are now ready to introduce the main objects studied in this paper.
From our point of view, graphons obtained by changing on a set of measure , or changing the -algebra so that remains measurable, do not differ essentially from the original. However, for technical reasons we have to distinguish them. We say that a graphon is strong, if is measurable with respect to (not just the completion of it). We can always change on a set of measure to make the graphon strong (Theorem 3.2(i)).
We say that is complete, if the underlying probability space is complete, and we say that it is Lebesguian, if the underlying probability space is a Lebesgue space. The completion, , of is obtained by completing the underlying probability space, i.e., by replacing by its completion .
Let be a graphon, and let be a finite graph with . The definition (1) then can be extended as
Let and be two graphons. The goal of this paper is to determine necessary and sufficient conditions under which
To this end, we will introduce two different notions of isomorphism. Both will be expressed in terms of the following operation: given a graphon and a measure preserving map from a probability space into , let be “pull-back” of , defined by . If and are two graphons and is measure preserving from the completion into such that almost everywhere, then we call a weak isomorphism from to . Note that a weak isomorphism is not necessarily invertible.
We say that and are isomorphic mod 0 (in notation ), if there exists a map such that is an isomorphism mod 0 and almost everywhere in . For simplicity, we often drop the qualifier mod 0.
We call and weakly isomorphic if there is a third graphon and weak isomorphisms from and into . It will follow from Theorems 3.2 and 2.1 that we could require here that is a strong Lebesguian graphon.
The isomorphism relation is clearly an equivalence relation, and it will follow from Theorem 2.1 (ii) below that weak isomorphism is an equivalence relation as well. Every graphon is weakly isomorphic with its completion, and every pair of isomorphic graphons is weakly isomorphic. It is clear that if two graphons and are weakly isomorphic then (4) holds for every graph . Theorem 2.1 (ii) below will show that the converse also holds.
To state our results, we need one more notion, the notion of twins. Let be a graphon. Two points are called twins if for almost all . Note that relation of being twins is an equivalence relation. We call the graphon almost twin-free if all there exists a set of measure zero such that no two points in are twins.
2 Main results
With these definitions, we can state our main result:
(i) If and are almost twin-free Lebesguian graphons, then (4) holds for every simple graph if and only if .
(ii) If and are general graphons, then (4) holds for every simple graph if and only if and are weakly isomorphic.
A natural idea of the proof of Theorem 2.1 is the following: can we bring a graphon to a “canonical form”, so that isomorphic or weakly isomorphic graphons would have identical canonical forms? In the case of functions in a single variable, this is possible, through “monotonization”: for every bounded real function on that has the same moments.
In Section 4 we’ll construct not quite a canonical form, but a “canonical ensemble”, a probability distribution of graphons on the same -algebra such that for almost all , and two graphons are isomorphic if and only if their ensembles can be coupled so that corresponding graphons are identical (up to sets of measure ).
An important element of the proof is a curious measure-theoretic fact. Consider a 2-variable function for which all 1-variable functions obtained by fixing one of the variables are measurable. This of course does not in general imply that the 2-variable function is measurable, but it does imply it in some circumstances (see e.g. Corollary 4.2).
As we will see, the second statement of Theorem 2.1 can easily be deduced from the first. In fact, we’ll show that every graphon is weakly isomorphic to a twin-free Lebesguian graphon. (See Theorem 3.2 for more details of this isomorphism.)
We can also transform a Lebesguian graphon into a graphon whose underlying probability space is the unit interval with the Lebesgue measure, by “resolving” the atoms into intervals of the appropriate length. This form is the most elementary and therefore useful in applications; however, it is not so convenient for the purposes of this paper because we loose twin-freeness.
It is easy to see that if and are weakly isomorphic, then (4) holds not only for simple graphs but also for graphs with multiple edges (which we’ll call multigraphs if we want to emphasize that multiple edges are allowed; but we don’t allow loops). Thus (4) for simple graphs implies this equation for multigraphs. (This fact will be an important step in the proof, see Section 5.2.)
We can formulate our results in a probabilistic way. Recall that a coupling between two probability spaces and is a probability distribution on whose marginals are and , respectively. A coupling between two graphons means a coupling between their underlying probability spaces. Let be a graphon, and let be independent random samples from . Then we have
Let and two graphons, and suppose that there exists a coupling between them such that holds with probability for two independent samples and from . In this case clearly (4) holds for every graph . As we will see, Theorem 2.1 implies that in the Lebesguian case the converse also holds.
For two functions the following are equivalent.
(a) For every simple graph , .
(b) For every multigraph , .
(c) There exists a function and two measure preserving maps such that and almost everywhere.
(d) There exist two measure preserving maps such that almost everywhere.
(e) There exists a probability measure on such that each marginal of is the Lebesgue measure, and if and are two independent samples from , then with probability .
3 Examples
The property of being twin-free is crucial for Theorem 2.1 (i).
Let be the map . For any function , the functions and define graphons that are weakly isomorphic but in general not isomorphic. Indeed, for a “generic” (say ), every point has two twins in and three twins in . The pair of maps in Corollary 2.2 (c) go from , while in (d), they go into .
Our next example shows that the Lebesgue property is also needed.
Let be a subset of $01\Omega^{\prime}{\cal A}{\cal A}^{\prime}\Omega\Omega^{\prime}WW^{\prime}xy\Omega\times\Omega\Omega^{\prime}\times\Omega^{\prime}\varphi:~\Omega\to\varphi^{\prime}:~\Omega^{\prime}\toH=(\Omega,{\cal A},\pi,W)H^{\prime}=(\Omega^{\prime},{\cal A}^{\prime},\pi^{\prime},W^{\prime})x\in\Omega$, we have
which shows that there is no way to “match up” the points in and to get an isomorphism mod . The same example shows that conclusions (d), (e) in Corollary 2.2 could not be extended to the non-Lebesgue case either.
Isomorphism
The main goal of this section is to describe how a general graphon can be transformed into a twin-free Lebesguian graphon. To this end, we have to recall some basic notions from measure theory (mostly because their usage does not seem standard), and then discuss different “isomorphism-like” mappings between graphons.
For a set of subsets of a set , we denote by the -algebra generated by . We call a -algebra countably generated if there is countable set such that . This is equivalent to the existence of a sequence of finite -algebras whose union generates .
We say that a set is a basis for the probability space , if is dense in , i.e., for every there is a such that .
Given sets and two points , we say that separates and if . We say that a set of subsets of separates and if there exists a set that separates and . This leads to a partition of by placing two points in the same class if and only if they are not separated by . We say that is separating if it separates any two points in . We’ll say that a graphon is separating if its underlying -algebra is separating.
A probability space is called a full subspace of if is a (not necessarily measurable) subset of of external measure , , and for all .
Consider two probability spaces and and a measure preserving map . The map is called an embedding of the first space into the second if is an isomorphism between and a full subspace of . We call an embedding of a graphon into a graphon if is an embedding of into and almost everywhere.
2 Push-Forward and Quotients
Let and be probability spaces and let be a measure preserving map. We have described how to “pull back” a graphon on to a (weakly isomorphic) graphon on . It is also possible to “push-forward” a graphon to a graphon . This is defined by the requirement that
for all . The next lemma states that the “push-forward” is well defined, and that is a certain conditional expectation of .
Let and be probability spaces, let be a measure preserving map, and let be a graphon on .
(ii) Let . Then almost everywhere.
(iii) If is an embedding of into , then almost everywhere.
(i) By linearity, it is easy to see that we can restrict ourselves to the case where takes values in $\mu{\cal A}^{\prime}\times{\cal A}^{\prime}$ by
for . With this definition, we have that
implying in particular that is absolutely continuous with respect to . Hence the Radon-Nikodym derivative,
is well defined. Using the above bound once more, together with the fact that , we furthermore have that
almost everywhere. Changing on a set of measure zero, we may assume that these relations hold everywhere. To define for a general bounded function , we use linearity.
(ii) Let , i.e., let and for some . By the definition of , the fact that is measure preserving, and the definition of , we have that
This implies that almost everywhere.
(iii) Since is an isomorphism between and a subspace of , we know that given any , we can find an such that . But then , proving that . Thus , which implies that almost everywhere. ∎
We can use the “push-forward” construction to define quotients of graphons. Let be a graphon, let be an arbitrary partition of into disjoint sets, and for , let denote the class in that contains the point . We then define a graphon and a measure preserving map as follows: the points in are the classes of the partition , is the map , is the -algebra consisting of the sets such that , and . Then is measure preserving, and the function is defined by (5).
3 Reductions
Now we are able to state the theorem that allows us to reduce every graphon to a twin-free Lebesguian graphon.
(i) Let be a graphon. Then one can change the value of on a set of -measure to get a strong graphon.
(ii) Let be a graphon. Then there exists a countably generated -algebra such that is -measurable.
(iii) Let be a graphon. Then the graphon is separating. If is countably generated, then so is .
(iv) Let be a separating graphon on a probability space with a countable basis. Then the completion of can be embedded into a Lebesguian graphon.
(v) Let be a graphon, and let be the partition into the twin-classes of . Then is almost twin-free. If is Lebesguian, then is Lebesguian as well. Furthermore, the projection is a weak isomorphism.
Every graphon has a weak isomorphism into a strong Lebesguian graphon.
The proof of this theorem (which is not hard, but technical) will be given in the rest of this section.
Let be a graphon, and let . Then is -measurable, and changing on a set of measure , we may assume that is symmetric and bounded. Moreover, for all , which implies that for all sets in the completion of , so almost everywhere. These observations prove part (i) of the Theorem.
3.2 Countable generation
We prove a simple lemma, which implies Theorem 3.2(ii), and will also be used at several other places (Sections 4.1 and 5.2).
Let be the set of bounded, -measurable functions for which the statement of the lemma is true. The set is clearly a vector space that contains the constant function as well as the indicator functions of all rectangles with and . If is further not hard to show that if is a sequence of non-negative functions in and for a bounded function , then the limiting function is in as well. By the monotone class theorem (see, e.g., Theorem 3.14 in ), we conclude that contains all bounded functions which are measurable with respect to the -algebra generated by the rectangles , i.e., the -algebra . ∎
3.3 Merging inseparable elements
If we identify elements in the same class of the partition , we get a -algebra which is isomorphic under the obvious map. This implies (iii) of Theorem 3.2.
3.4 Lebesgue property
Consider a separating graphon , and assume that is generated by the countable set . Then is a basis for the completion of . We invoke the fact (see e.g. , Section 2.2) that any separating complete probability space with a countable basis can be embedded into a Lebesgue space. Thus there exists an embedding of the completion of into a Lebesgue space . Let be the push-forward of , . By Lemma 3.1, we have that almost everywhere, which shows that is an embedding of the completion of into . This proves part (iv) of Theorem 3.2.
3.5 Partitions into Twin-Classes
We prove (v) in Theorem 3.2. We may assume that is countably generated. Indeed, by Lemma 3.4, we can replace by a countably generated -algebra . This does not change the relation of being twins: Two points are twins if and only if the set has measure . Since is measurable with respect to , the set lies in , implying that and are twins with respect to if and only if they are twins with respect to .
Let consists of those sets in that do not separate any pair of twin points. Clearly is a -algebra.
is almost -measurable.
Let . We want to prove that
for all . Define the functions
Since if are twins, the function is -measurable, similarly for , and obviously for . Repeatedly using the fact that if is -measurable, this implies
(where the last equality follows since is -measurable). This implies (8) and completes the proof of Claim 1.
Let as before, then is a graphon, which is clearly weakly isomorphic to . Let be the set of points for which has positive measure. Then clearly is a null set, and two points are twins in if and only if they are twins in . The graphon is obtained from by identifying indistinguishable elements, which implies that is twin-free.
To prove that is Lebesguian if is Lebesguian, we invoke the fact (established in Section 3.2 of ) that is a Lebesgue space provided is a Lebesgue space and there exists a countable set that separates two points if and only they are in different partition classes.
To construct such a set , let be a countable set generating , closed under finite intersections. For and , let
Since is a bounded -measurable function, the function is a finite measure for all , while the function is a -measurable function on for all .
By definition, are twins iff the set has measure zero. This is equivalent to the condition that for all . Since the measure on is uniquely determined by the sets in , we have that and are twins if and only if for all .
For every and rational number , consider the sets . There is a countable number of these. Furthermore, if and are twins, then they belong to exactly the same sets ; if they are not twins, then there is a such that , and for any rational number between and , the set separates and .
4 Isomorphism and Weak Isomorphism
We conclude this section with relating isomorphism and weak isomorphism.
Let be graphons with the Lebesgue property (), and let be measure-preserving. If is almost twin-free, and almost everywhere, then is an isomorphism mod , so in particular .
and let . Then by Fubini and our assumption that almost everywhere.
Let be a nullset such that all twin-classes of have at most one point in , and let to be the restriction of to . Then is injective: indeed, if and , then for almost all by the definition of , hence and are twins, a contradiction. As shown in , Section 2.5, an injective measure preserving map between Lebesgue spaces has a measurable inverse defined almost everywhere. This implies that is an isomorphism mod , which shows that is an isomorphism mod as well. ∎
If two twin-free graphons with the Lebesgue property are weakly isomorphic, then they are isomorphic.
Canonical Ensembles
We could try to construct a “canonical form” of a graphon by assigning “tags” to the points in . For example, we could tag a point with its marginal , or by the sequence of marginals of higher powers of . This, however, would not work: for example, there could be a transitive group of measure-preserving permutations of leaving invariant, and then all points would still have the same tag.
To break the symmetry, we select an infinite sequence of points in , which we call anchor points. Now we can tag each point with the sequence
We will show that if are taken i.i.d. at random with distribution then with probability one, then is isomorphic mod to the original graphon (see Section 4.2 for details). So using an infinite sequence of independent random points as anchor points, the tags of the points contain all information about the points.
These tags are almost canonical, except for the choice of the sequence . So instead of a canonical form, we get a “canonical ensemble”, a probability distribution of graphons such that for almost all , and two graphons are isomorphic if and only if their ensembles can be coupled so that corresponding graphons are isomorphic.
To prove Theorem 2.1 (i), we will therefore have to show that if and satisfy (4), then we can “couple” the choice of anchor points in and in so that , thus yielding an isomorphism of and . This second step in the proof will be carried out in Section 5.3.
The next technical lemma will be important in the construction of “canonical ensembles”.
By Lemma 3.4, we may assume that and are countably generated. Let and be a sequence of finite -algebras with and , and let denote the partition of into the atoms of . For with , define
We define if with .
First we prove that for every , every and , we have with probability
It suffices to prove this in the case when and . Then for every , we have
Since both sides are independent of , integrating over equation (11) follows.
The number of choices of , and is countable, and hence it follows that with probability 1, (11) holds for all , every and . Since is dense in , this implies that (11) holds for all , every and .
From now on, we suppose that the choice of the is such that this holds.
For a fixed , the indices have a subsequence such that converges to some function in the weak--topology of . Hence by (11),
for all , every and . Thus is a representative of . Since is measurable, it is also a representative of . This shows that for every we have
By Levy’s Upward Theorem, the left hand side of (12) tends to almost everywhere. The right hand side of (12) tends to almost everywhere, so almost everywhere, which proves the Lemma. ∎
We formulate a couple of corollaries, the first of which is immediate:
Let be a graphon, and let be independent random points from . Let be the (random) -algebra generated by the functions . Then with probability , is almost -measurable.
Let denote the -algebras generated by the functions . Clearly . By Lemma 4.1, is almost measurable with probability , so we can change it on a set of measure to get an measurable function . Let be the -algebras generated by the functions . Applying the lemma again, we get that is almost measurable. With probability , each function differs from on a set of measure only (since the are independent of ), and so . So is measurable, which implies that , and hence , are almost measurable. ∎
2 Anchor Sequences
Let denote the -algebra of subsets of of the form , where . Note that by the fact that is measurable. Further, almost by definition, is the smallest sub--algebra of such that all the functions are measurable. As a consequence, we may apply Lemma 4.3 to conclude that for almost all , is almost -measurable, which by Lemma 3.1 gives that almost everywhere. ∎
Let be a twin free graphon with the Lebesgue property. If is regular, then is an isomorphism mod and .
Coupling
We recall some notions from . A partially labeled graph is a finite graph in which some of the nodes are labeled by different nonnegative integers. Two partially labeled graphs are isomorphic, if there is a label-preserving isomorphism between them. A -labeled graph is a partially labeled graph with labels .
Let and be two partially labeled graphs. Their product is defined as follows: we take their disjoint union, and then identify nodes with the same label (retaining the labels, and any multiple edges which this might create). For two unlabeled graphs, is their disjoint union. Clearly this multiplication is associative and commutative.
It is easy to see that if and are two -labeled graphs, then
2 Multiple Edges
Let and be two graphons, and assume that for every simple graph . Then for every multigraph .
We use induction on the number of parallel edges in . Suppose that has two nodes, say and , connected by more than one edge. Let denote the multigraph obtained from by subdividing one of these edges by new nodes. Let denote the multigraph obtained by removing one copy of the edge . So , but for , has fewer parallel edges than , and so we may assume that
holds for every . We consider all the multigraphs and as -labeled graphs, with and labeled and .
Since can be thought of as the product of and a path with nodes (the endpoints labeled), we can write
The first factor inside the integral can be expressed as
which we can recognize as -th power of the kernel as an integral operator.
At this point, it will be useful to assume that and are countably generated graphons (this can be done without loss of generality by Lemma 3.4). As a consequence, is an integral operator on the separable Hilbert space , and since is bounded, this implies that is Hilbert-Schmidt and thus compact, which in turn implies that has a spectral representation:
be the spectral representation of , then we get that for every ,
are independent of . (The integrals exist since is a bounded function of and .) It follows that in (14) everything must cancel, in other words, for every value ,
(it is known that the sums on both sides have a finite number of terms, since the multiplicities of the eigenvalues are finite).
Now while (13) may not be true with equality, the “trace” with any other kernel gives an equation; in particular,
which shows that as claimed. ∎
It will be convenient to assume that . If this does not hold, we can apply a linear transformation to the values of the functions, to get two functions and with . Expanding the product in the definition (3), can be written as a linear combination of the values , where is a subgraph of . Thus for every graph if and only if for every graph (where “graph” could mean either simple graph or multigraph). So (4) holds for and if and only if it holds for and . If we prove that this implies , then follows trivially.
3 Coupling Anchor Sequences
The condition on the coupling is described in the following lemma.
Let and be two graphons, and let and be regular sequences for and , respectively. Suppose that for every partially labeled multigraph ,
Then and almost everywhere (with respect to ).
First, we show that . These probability measures are defined on the -algebra as the distribution measures of the random variables and , where and are random points from and , respectively. By Lemma 6.1 it therefore suffices to prove that these random variables have the same mixed moments.
Let be a sequence of nonnegative integers, of which only a finite number is nonzero; say for . Then
where is the star on nodes, with the endnodes labeled , and the edge between the center and endnode replaced by parallel edges. Similarly,
These numbers are equal by the hypothesis of the Lemma. This proves that .
We can generate by choosing independent uniform random points and from , and letting and . Since is regular, we have that
where and are independent random points from . To prove that and have the same distribution, it again suffices to prove that they have the same mixed moments.
A particular mixed moment is given by nonnegative integers , and (of which only a finite number is nonzero; say for ). Let us define the multigraph as follows. has two unlabeled nodes and , and further nodes labeled . We connect to by edges, to by edges (), and to by edges. Then
These two numbers are the same by hypothesis. This completes the proof of the Lemma. ∎
Let and be two Lebesguian graphons such that
holds almost surely for every partially labeled multigraph .
Let be the set of -labeled multigraphs. We define recursively a coupling of sequences with sequences so that holds almost surely for every . Let and be chosen from this coupled distribution. Consider two random points from and from , and the random variables
with values in . We claim that the variables and have the same distribution. It suffices to show that and have the same mixed moments. Consider any moment of ; in other words, let , let be nonnegative integers, and let be obtained from by replacing each edge in by edges. Then the corresponding moment of is
where the multigraph is obtained by unlabeling the node labeled in the multigraph . Expressing the moments of in a similar way, we see that they are equal by the induction hypothesis. This proves that and have the same distribution.
Using Lemma 6.2 it follows that we can couple the variables and so that with probability 1. In other words, we can replace and by a random variable so that has distribution , has distribution , and their joint distribution satisfies
for every with probability 1. Thus we have extended the coupling to .
4 Conclusion of proofs
Proof of Theorem 2.1. Part (i) follows easily: if we choose random sequences from the coupled distribution given by Lemma 5.3, then these sequences will be regular with probability , and so they satisfy the conditions of Lemma 5.2.
To prove (ii), suppose that and satisfy (4) for every simple graph . By Corollary 3.3, we can find twin-free Lebesguian graphons and and weak isomorphisms and from and to and , respectively. It follows by Theorem 2.1(i) that the and are isomorphic mod , so in particular almost everywhere for some measure preserving map . Defining by , we conclude that almost everywhere. The maps and are measure preserving from the completions and into .
Proof of Corollary 2.2. The equivalence of (a), (b) and (c) follows by Theorem 2.1 (ii) and the fact that a function which is measurable with respect to the completion of is almost everywhere equal to a function which is measurable with respect to . In the proof of (c), Theorem 2.1 may give a graphon containing atoms, but it is easy to replace these atoms by intervals of appropriate length.
To prove that (c)(e), assume that and exist as in (c). Let be independent random points from the uniform distribution on $\varphi\psi\varphi(X)\psi(Y)\gamma\times\lambda(X,X^{\prime})\gamma\varphi(X)=\psi(X^{\prime})1(X,X^{\prime})(Y,Y^{\prime})\gamma$, then
To prove that (e)(d), consider the projections defined by and . Then
Thus, almost everywhere. Furthermore, and are measure preserving if we consider the coupling measure on $$.
Since the completion of is a Lebesgue space, we can find a measure preserving map . Setting and , we obtain the desired measure preserving maps such that almost everywhere.
Finally, (d)(a) is trivial.
Acknowledgement
We are grateful to Miklós Laczkovich, Ron Peled, Yuval Peres and Oded Schramm for many useful discussions on the topic of this paper, and to Kati Vesztergombi and Svante Janson for carefully reading an earlier version and suggesting several improvements.
References
Appendix: Moments and coupling of probability distributions
In this section we prove some probability theory lemmas, that are “well known” but not easy to reference. We start with the fact that if two vector valued random variables have the same mixed moments, then they have the same distribution (cf Feller , Problem XV.9.21.).
It suffices to prove that for every Borel set of the form , where are intervals. Let be a polynomial that approximates the indicator function on $L_{1}1/m(j=1,\dots,n)$. Then
But the left hand sides of these two relations are equal for all , which proves the Lemma. ∎
We need the following natural fact about coupling.
Assume that and are Lebesgue spaces, and , a countably generated separating space. Let and be measure preserving maps. Then there exists a coupling of and such that
For , consider the measure defined for , and its Radon-Nikodym derivative . Since , this derivative exists, and almost everywhere. Furthermore, and almost everywhere.
Similarly, for , define and . Finally, let
and similarly . Hence in particular if either or .
If , () and the sets form a (finite or countably infinite) partition of (, ), then .
It is easy to see that if are disjoint sets and , then almost everywhere. It follows that for every , we have . This implies by standard arguments that the claim holds if is finite. This in turn implies that extends to a finitely additive measure on the algebra of sets that can be written as the union of a finite number of product sets (, ).
In the case of infinite , it follows that ; in fact, for every finite , we have , and hence by the finite additivity of , we have
Since this holds for every finite subset of , it also holds for .
on a set of positive measure. Now we use that and are Lebesgue spaces, so we may assume that they are intervals and respectively, together with a countable set of atoms. Thinking of the atoms as converging to from above, we have a compact topology on them. For every , we can find an open sets and such that and . Also, we can find closed sets and such that and . Then
The open sets cover the compact set , and so a finite number of them also covers. But the contradicts the finite additivity of which we already established.
The setfunction extends to a measure on .
We have seen already that extends to ; it follows by Claim 2 that this extension is -additive. Thus the Claim follows by the Measure Extension Theorem.
Define . To complete the proof of the Lemma, we want to prove that is a coupling between and (which is trivial), and that . Let be a countable family separating the elements of . Then
Consider any term here, say . Then
This proves that . ∎