4 Convergence of the structure of a uniform well-labeled g-tree
4.1 Preliminaries
We investigate here the convergence of the integers previously defined, suitably rescaled, in the case of a uniform random well-labeled g-tree with n vertices. Let t
n
, l
n
be uniformly distributed over the set
T
n
of well-labeled g-trees with n vertices. We call its scheme s
n
and we define M
e n
e ∈~Es
n
, f
e n
, l
e n
e ∈~Es
n
, m
e n
e ∈~Es
n
, σ
e n
e ∈~Es
n
, l
e n
e ∈~Es
n
, l
v n
v ∈Vs
n
, and u
n
as in the previous section. We know that the right scalings are 2n for sizes, p
2n for distances in the g
-tree, and γ n
1 4
for spatial displacements
3
, so we set: m
e n
:= 2m
e n
+ σ
e n
2n ,
σ
e n
:= σ
e n
p 2n
, l
e n
:= l
e n
γ n
1 4
, l
v n
:= l
v n
γ n
1 4
and u
n
:= u
n
2n .
Remark. Throughout this paper, the notations with a parenthesized n will always refer to suitably
rescaled objects—as in the definitions above. As sensed in the previous section, it will be more convenient to work with l
v
’s instead of l
e
’s. We use the notation Z
+
:= {0, 1, . . . } for the set of non-negative integers. For any scheme s ∈ S, we define the set C
n
s of quadruples m, σ, l, u lying in Z
~ Es
+
× N
~ Es
× Z
V s
× Z
+
such that: ⋄ ∀e ∈ ~Es, σ
¯e
= σ
e
, ⋄ l
e
− ∗
= 0, ⋄ 0 ≤ u ≤ 2m
e
∗
+ σ
e
∗
− 1, ⋄
P
e ∈~Es
m
e
+
1 2
σ
e
= n.
This is the set of integers satisfying the constraints discussed in the previous section for a well- labeled g-tree with n edges. For m,
σ, l, u ∈ C
n
s, we will compute the probability that s
n
= s and m
n
, σ
n
, l
n
, u
n
= m, σ, l, u. A g-tree has such features if and only if its scheme is s and, for every
e ∈ ~Es, the path M
e
is a Motzkin bridge from 0 to l
e
= l
e
+
− l
e
−
on [0, σ
e
], and the well-labeled forest f
e
, l
e
lies in F
m
e
σ
e
. Moreover, because of the relation 9, the Motzkin bridges M
e e
∈~Es
are entirely determined by M
e e
∈ ˇEs
, where ˇ Es is any orientation of ~
Es. Using Lemma 3, we obtain P
s
n
= s, m
n
, σ
n
, l
n
, u
n
= m, σ, l, u
= 1
T
n
Y
e ∈ ˇEs
M
→l
e
[0, σ
e
]
F
m
e
σ
e
F
m
¯ e
σ
¯ e
= 12
n
T
n
Y
e ∈~Es
σ
e
2m
e
+ σ
e
P S
2m
e
+ σ
e
= σ
e
Y
e ∈ ˇEs
P M
σ
e
= l
e
12
3
Recall that γ :=
8 9
1 4
.
1608
where S
i i
≥0
is a simple random walk on Z and M
i i
≥0
is a simple Motzkin walk. We will need the following local limit theorem see [25, Theorems VII.1.6 and VII.3.16] to estimate
the probabilities above. We call p the density of a standard Gaussian random variable: px =
1 p
2 π
e
−
x2 2
.
Proposition 6. Let X
i i
≥0
be a sequence of i.i.d. integer-valued centered random variables with a moment of order r
for some r ≥ 3. Let η
2
:= VarX
1
, h be the maximal span
4
of X
1
and the integer a be such that a.s. X
1
∈ a + hZ. We define Σ
k
:= P
k i=
X
i
, and we write Q
Σ k
i := PΣ
k
= i. 1. We have
sup
i ∈ka+hZ
η h
p k Q
Σ k
i − p
i η
p k
= o
k
−12
.
2. For all 2
≤ r ≤ r , there exists a constant C such that for all i
∈ Z and k ≥ 1, η
h p
k Q
Σ k
i ≤
C 1 +
i η
p k
r
.
Proof. The first part of this theorem is merely [25, Theorem VII.1.6] applied to the variables
1 h
X
k
− a, which have 1 as maximal span. The second part is an easy consequence of [25, Theorem VII.3.16].
In what follows, we will always use the notation S for simple random walks, M for simple Motzkin
walks, and Σ for any other random walks. We will use this theorem with S and M : we find η, h =
1, 2 for S and η, h =
p 2
3, 1 for M. In both cases, we may take r as large as we want.
4.2 Result
Kategori