Trees and Branching Processes

Brigitte Chauvin

Université de Versailles-St Quentin

Algorithms Seminar

January 5, 1998

[summary by Philippe Robert]

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Abstract
A random tree is defined as an elementary event w of a probability space (W , F, P). The probability P depends on the random model of trees which is analyzed. The main results concerning the Galton-Watson processes are recalled. If for nÎN, Zn is the number of individuals of the N-th generation and m the average number of children generated by an individual, it is shown that the martingale (Zn/mn) plays an important role in the analysis of such processes.

The Catalan trees are seen as a particular case of Galton-Watson process. The height of a Catalan tree with n nodes is of the order C (n)1/2 (Flajolet-Odlyzko) and the number of external leaves has a limiting distribution (Kesten-Pittel).

The binary search trees are related to a branching random walk, hence to marked trees. The analysis of their height involves large deviations results for this random walk; for a binary search tree with n nodes, it is of the order C log n (Devroye, Biggins).

## 1   Probabilistic Model

Definition 1   If Q=(qi) is a probability distribution on N (qi³ 0 for i³ 0 and åi=0+¥ qi=1), a Galton-Watson process with generating distribution Q is a sequence of random variables (Zn) defined by
Z0=1,     Zn+1=
 Zi å i=1
Gi n,
where the (Gij), i,jÎN are independent identically distributed random variables with distribution Q.
For nÎN, Zn is the number of individuals at the n-th generation. By convention the generation 0 contains only the ancestor (Z0=1) and the i-th individual of the n-th generation has Gin children.

The underlying tree structure of such a process is obvious. It is nevertheless convenient to reformulate these processes within the framework of trees . A tree w is a subset of
U={Ø}È
 È n³ 1
N*n
with the following properties:
1. ØÎw, i.e. the ancestor is in the tree;
2. If u· vÎ w, then uÎw, (u· v denotes the concatenation of strings);
3. If uÎ w then there exists Nu(w)ÎN such that u· jÎw if and only if 1£ j£ Nu(w). The variable Nu(w) is the number of children of the node u. By convention NØ=N.
With this notation, the tree of the Figure 1 can be represented as
 w= { Ø, 1 , 2, 3, 21, 211, 2111, 2112, 22, 221, 31, 311 } .
If uÎ U, |u| will denote the length of the string u, in particular
H(w)=sup{|u|, uÎw},
is the height of the tree w and if zn(w)= {uÎw,|u|=n}, then Zn(w)=Card(zn(w)) is the number of individuals of generation n. Figure 1: Trees as subsets of U

Finally, if uÎw, Tu(w) will denote the subtree containing the elements of w whose prefix is u. In the example of Figure 1,
 T21(w)= { Ø, 1 , 11, 12 } .
Definition 2   A Galton-Watson tree with generating distribution Q is a probability distribution P on the set of trees such that
1. P(N=k)=qk;
2. Conditionally on the event {N(w)=n}, the subtrees T1(w), T2(w), ..., Tn(w) are independent with distribution P.
The first condition says that the number of children of the ancestor has distribution Q. The other condition gives an homogeneity property (the subtree Ti(w) and w have the same distribution for i£ n). The independence of the behavior of the individuals, corresponds to the independence of the Gi1, i=1,...,n in our first definition. From now on, (Zn) denotes a Galton-Watson process associated to Q.

## 2   Limiting Behavior of Galton-Watson Trees

Notice that if q0=P(N=0)>0, then it is possible that an individual does not generate children at all. Consequently, a complete extinction of the family of the ancestor is also possible. The following proposition describes this phenomenon.
Proposition 1   If m=E(G11)=åi=0+¥iqi is the average number of children per individual, then
P æ
ç
ç
è
 +¥ å n=0
Zn<+¥ ö
÷
÷
ø
=q,
where q is the smallest solution sÎ[0,1] of the equation åi=0+¥qi si=s. If m£ 1 , the Galton-Watson becomes extinct with probability 1, that is, q=1; and if m> 1 then q<1.

We can now state a classical theorem for Galton-Watson processes.
Theorem 1   The process (Wn)=(Zn/mn) is a positive martingale with expected value 1, furthermore the sequence (Wn) is almost surely converging to a finite random variable W.

### Refinements

The following theorems give more insight on the behavior on the sequence (Zn). There are three theorems, one for each of the three cases m>1, m=1 and m<1.
Theorem 2  [Kesten-Stigum ]   If m>1, the following conditions are equivalent
1. (Zn/mn) converge to W in L1(P);
2. E(Nlog N)=åi=2+¥ klog(k) qk<+¥;
3. P(W=0)=q.
The above result is mainly a strengthening of Theorem 1. It can be proved in an elegant way  with the formalism we described in the introduction. This proof uses a change of probability and the martingale (Wn).

The following theorem is more informative from a qualitative point of view. It says that in the critical case (m=1) the variable Zn grows linearly conditionally on {Zn>0} (remember that in this case, almost surely Zn=0 for n large enough).
Theorem 3   If m=1 and s=Var(N)<+¥, conditionally on the event {Zn>0}, the random variable Zn/n converges in distribution to an exponential distribution with parameter s/2.
The same conditioning procedure as in the critical case does not lead to the same phenomenon in the sub-critical case (m<1). Basically the conditioned variable Zn stays bounded.
Theorem 4  [Yaglom ]   If 0<m<1, then conditionally on the event {Zn>0}, the random variable converges in distribution to a finite random variable.

## 3   Catalan Trees, Dyck Paths and Galton-Watson Processes

Definition 3
1. A Catalan tree with n nodes is a random tree for the uniform distribution, that is, the probability of a tree w is P(w)= (n+1)/(
 2n n
), if Card(w)=n and 0 otherwise.
2. A Dyck path of length 2n is a positive path with the jumps 1, -1 starting at 0 and finishing at 0 for the 2n-th jump.
3. An excursion of the simple random walk is the trajectory of the walk until it reaches 0 for the first time. A simple random walk is a walk which starts at 0 and whose jumps are 1 and -1 and equally likely.
Proposition 2
• The set of Catalan trees of size n and the set of Dyck paths with 2n steps have the same cardinality.
• The Galton-Watson process with Q=(1/2i) and the excursions of the simple random walk are isomorphic, i.e. there is a bijection which maps a Galton-Watson process to an excursion and preserves the distributions.

Proof. The picture below shows how an excursion is transformed into a Galton-Watson process. Figure 2: Equivalence between Galton-Watson processes and excursions

Remark 1   If one draws a contour starting at the left of the root of the tree in Figure 2 and following the vertices of the tree, when the contour arrives on the right of the root, its height will have performed the path followed by the random walk of Figure 2 above 1.

## References


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Lyons (Russell), Pemantle (Robin), and Peres (Yuval). -- Conceptual proofs of Llog L criteria for mean behavior of branching processes. The Annals of Probability, vol. 23, n°3, 1995, pp. 1125--1138.


Neveu (Jacques). -- Arbres et processus de Galton-Watson. Annales de l'Institut Henri Poincaré. Probabilités et Statistique, vol. 22, n°2, 1986, pp. 199--207.


Yaglom (A. M.). -- Certain limit theorems of the theory of branching random processes. Doklady Akad. Nauk SSSR (N.S.), vol. 56, 1947, pp. 795--798.

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