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Brownian excursions, critical random graphs and the multiplicative coalescent, (1997)

by D Aldous
Venue:Ann. Probab.
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Deterministic and Stochastic Models for Coalescence (Aggregation, Coagulation): a Review of the Mean-Field Theory for Probabilists

by David J. Aldous - Bernoulli , 1997
"... Consider N particles, which merge into clusters according to the rule: a cluster of size x and a cluster of size y merge at (stochastic) rate K(x; y)=N , where K is a specified rate kernel. This MarcusLushnikov model of stochastic coalescence, and the underlying deterministic approximation given by ..."
Abstract - Cited by 222 (13 self) - Add to MetaCart
Consider N particles, which merge into clusters according to the rule: a cluster of size x and a cluster of size y merge at (stochastic) rate K(x; y)=N , where K is a specified rate kernel. This MarcusLushnikov model of stochastic coalescence, and the underlying deterministic approximation given by the Smoluchowski coagulation equations, have an extensive scientific literature. Some mathematical literature (Kingman's coalescent in population genetics; component sizes in random graphs) implicitly studies the special cases K(x; y) = 1 and K(x; y) = xy. We attempt a wide-ranging survey. General kernels are only now starting to be studied rigorously, so many interesting open problems appear. Keywords. branching process, coalescence, continuum tree, densitydependent Markov process, gelation, random graph, random tree, Smoluchowski coagulation equation Research supported by N.S.F. Grant DMS96-22859 1 Introduction Models, implicitly or explicitly stochastic, of coalescence (= coagulati...
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...esponding to the gelation time T gel = 1 in the deterministic model. Rather detailed rigorous results are known: see [43] for recent exhaustive analysis. We give a probabilistic discussion, following =-=[9]-=-. Recall ML (N) 1 (t) is the mass of the largest cluster in the Marcus-Lushnikov process, that is the size of the largest component of G(N; 1 \Gamma e \Gammat=N ). It is classical [28, 18] that ML (N)...

Coalescents With Multiple Collisions

by Jim Pitman - Ann. Probab , 1999
"... For each finite measure on [0 ..."
Abstract - Cited by 183 (11 self) - Add to MetaCart
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...ISIONS Jim Pitman Technical Report No. 495 Department of Statistics, University of California 367 Evans Hall # 3860, Berkeley, CA 94720-3860 Revised March 23, 1999 Abstract For each finite measureson =-=[0; 1]-=-, a coalescent Markov process, with state space the compact set of all partitions of the set N of positive integers, is constructed so the restriction of the partition to each finite subset of N is a ...

The Standard Additive Coalescent

by David Aldous, Jim Pitman , 1997
"... Regard an element of the set \Delta := f(x 1 ; x 2 ; : : :) : x 1 x 2 : : : 0; X i x i = 1g as a fragmentation of unit mass into clusters of masses x i . The additive coalescent of Evans and Pitman (1997) is the \Delta-valued Markov process in which pairs of clusters of masses fx i ; x j g mer ..."
Abstract - Cited by 87 (21 self) - Add to MetaCart
Regard an element of the set \Delta := f(x 1 ; x 2 ; : : :) : x 1 x 2 : : : 0; X i x i = 1g as a fragmentation of unit mass into clusters of masses x i . The additive coalescent of Evans and Pitman (1997) is the \Delta-valued Markov process in which pairs of clusters of masses fx i ; x j g merge into a cluster of mass x i +x j at rate x i +x j . They showed that a version (X 1 (t); \Gamma1 ! t ! 1) of this process arises as a n !1 weak limit of the process started at time \Gamma 1 2 log n with n clusters of mass 1=n. We show this standard additive coalescent may be constructed from the continuum random tree of Aldous (1991,1993) by Poisson splitting along the skeleton of the tree. We describe the distribution of X 1 (t) on \Delta at a fixed time t. We show that the size of the cluster containing a given atom, as a process in t, has a simple representation in terms of the stable subordinator of index 1=2. As t ! \Gamma1, we establish a Gaussian limit for (centered and norm...

A classification of coalescent processes for haploid exchangeable population models

by Martin Möhle, Serik Sagitov - Ann. Probab , 2001
"... We consider a class of haploid population models with non-overlapping generations and fixed population size N assuming that the family sizes within a generation are exchangeable random variables. A weak convergence criterion is established for a properly scaled ancestral process as N! 1. It results ..."
Abstract - Cited by 63 (4 self) - Add to MetaCart
We consider a class of haploid population models with non-overlapping generations and fixed population size N assuming that the family sizes within a generation are exchangeable random variables. A weak convergence criterion is established for a properly scaled ancestral process as N! 1. It results in a full classification of the coalescent generators in the case of exchangeable reproduction. In general the coalescent process allows for simultaneous multiple mergers of ancestral lines.

Coalescent Random Forests

by Jim Pitman - J. COMBINATORIAL THEORY A , 1998
"... Various enumerations of labeled trees and forests, including Cayley's formula n n\Gamma2 for the number of trees labeled by [n], and Cayley's multinomial expansion over trees, are derived from the following coalescent construction of a sequence of random forests (R n ; R n\Gamma1 ; : ..."
Abstract - Cited by 53 (14 self) - Add to MetaCart
Various enumerations of labeled trees and forests, including Cayley's formula n n\Gamma2 for the number of trees labeled by [n], and Cayley's multinomial expansion over trees, are derived from the following coalescent construction of a sequence of random forests (R n ; R n\Gamma1 ; : : : ; R 1 ) such that R k has uniform distribution over the set of all forests of k rooted trees labeled by [n]. Let R n be the trivial forest with n root vertices and no edges. For n k 2, given that R n ; : : : ; R k have been defined so that R k is a rooted forest of k trees, define R k\Gamma1 by addition to R k of a single edge picked uniformly at random from the set of n(k \Gamma 1) edges which when added to R k yield a rooted forest of k \Gamma 1 trees. This coalescent construction is related to a model for a physical process of clustering or coagulation, the additive coalescent in which a system of masses is subject to binary coalescent collisions, with each pair of masses of magnitude...

The birth of the infinite cluster: finite-size scaling in percolation.

by C Borgs , J T Chayes , H Kesten , J Spencer - Commun. Math. Phys., , 2001
"... Abstract. We address the question of finite-size scaling in percolation by studying bond percolation in a finite box of side length n, both in two and in higher dimensions. In dimension d = 2, we obtain a complete characterization of finite-size scaling. In dimensions d > 2, we establish the sam ..."
Abstract - Cited by 48 (6 self) - Add to MetaCart
Abstract. We address the question of finite-size scaling in percolation by studying bond percolation in a finite box of side length n, both in two and in higher dimensions. In dimension d = 2, we obtain a complete characterization of finite-size scaling. In dimensions d > 2, we establish the same results under a set of hypotheses related to so-called scaling and hyperscaling postulates which are widely believed to hold up to d = 6. As a function of the size of the box, we determine the scaling window in which the system behaves critically. We characterize criticality in terms of the scaling of the sizes of the largest clusters in the box: incipient infinite clusters which give rise to the infinite cluster. Within the scaling window, we show that the size of the largest cluster behaves like n d π n , where π n is the probability at criticality that the origin is connected to the boundary of a box of radius n. We also show that, inside the window, there are typically many clusters of scale n d π n , and hence that "the" incipient infinite cluster is not unique. Below the window, we show that the size of the largest cluster scales like ξ d π ξ log(n/ξ), where ξ is the correlation length, and again, there are many clusters of this scale. Above the window, we show that the size of the largest cluster scales like n d P ∞ , where P ∞ is the infinite cluster density, and that there is only one cluster of this scale. Our results are finite-dimensional analogues of results on the dominant component of the Erdős-Rényi mean-field random graph model.
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...ant θ(c) > 0, with probability one, while for c = 1, W (1) has a nontrivial distribution (i.e., W (1) /N 2/3 � constant) ([ER59], [ER60],sFINITE-SIZE SCALING IN PERCOLATION, December 2000 3 [JKLP93=-=], [Ald97]). F-=-or c ≤ 1, the sizes of the second, third, . . . , largest clusters are of the same scale as that of the largest cluster, while for c > 1 this is not the case: For any fixed i > 1, W (i) ≍ log N fo...

Construction Of Markovian Coalescents

by Steve N. Evans, Jim Pitman - Ann. Inst. Henri Poincar'e , 1997
"... Partition-valued and measure-valued coalescent Markov processes are constructed whose state describes the decomposition of a finite total mass m into a finite or countably infinite number of masses with sum m, and whose evolution is determined by the following intuitive prescription: each pair of ma ..."
Abstract - Cited by 48 (16 self) - Add to MetaCart
Partition-valued and measure-valued coalescent Markov processes are constructed whose state describes the decomposition of a finite total mass m into a finite or countably infinite number of masses with sum m, and whose evolution is determined by the following intuitive prescription: each pair of masses of magnitudes x and y runs the risk of a binary collision to form a single mass of magnitude x+y at rate (x; y), for some non-negative, symmetric collision rate kernel (x; y). Such processes with finitely many masses have been used to model polymerization, coagulation, condensation, and the evolution of galactic clusters by gravitational attraction. With a suitable choice of state space, and under appropriate restrictions on and the initial distribution of mass, it is shown that such processes can be constructed as Feller or Feller-like processes. A number of further results are obtained for the additive coalescent with collision kernel (x; y) = x + y. This process, which arises fro...
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...efined by a finite number of masses evolving with the same collision rate kernel . We assume throughout that our system has a finite total mass m. By scaling, we can assume m = 1. But see also Aldous =-=[1]-=-, who obtains interesting results for the multiplicative coalescent with collision rate (x; y) = xy in a system with infinite total mass. Informally, we regard a -coalescent as an evolving family of a...

The Wiener Index Of Simply Generated Random Trees

by Svante Janson - Random Struct. Alg , 2003
"... Asymptotics are obtained for the mean, variance and higher moments as well as for the distribution of the Wiener index of a random tree from a simply generated family (or, equivalently, a critical Galton-- Watson tree). We also establish a joint asymptotic distribution of the Wiener index and the in ..."
Abstract - Cited by 40 (10 self) - Add to MetaCart
Asymptotics are obtained for the mean, variance and higher moments as well as for the distribution of the Wiener index of a random tree from a simply generated family (or, equivalently, a critical Galton-- Watson tree). We also establish a joint asymptotic distribution of the Wiener index and the internal path length, as well as asymptotics for the covariance and other mixed moments. The limit laws are described using functionals of a Brownian excursion. The methods include both Aldous' theory of the continuum random tree and analysis of generating functions. 1.
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...combinatorial problems, see 2 again [13] and the references cited there. We do not know any similar other results involving η or ζ. There is a simple connection, discovered by Spencer [23] and Aldou=-=s [4], between th-=-e moments of ξ and Wright’s constants in the enumeration of connected graphs with n vertices and n + k edges [28]. In fact, ρk and σk in [28] are given by ρk−1 = E(ξ/2) k /k! and ω∗ k0 = 2...

Percolation on transitive graphs as a coalescent process: relentless merging followed by simultaneous uniqueness

by Yuval Peres, Roberto H. Schonmann - Perplexing Problems in Probability: Festschrift in Honor of Harry Kesten , 1999
"... Consider i.i.d. percolation with retention parameter p on an in-finite graph G. There is a well known critical parameter pc ∈ [0, 1] for the existence of infinite open clusters. Recently, it has been shown that when G is quasi-transitive, there is another critical value pu ∈ [pc, 1] such that the nu ..."
Abstract - Cited by 39 (9 self) - Add to MetaCart
Consider i.i.d. percolation with retention parameter p on an in-finite graph G. There is a well known critical parameter pc ∈ [0, 1] for the existence of infinite open clusters. Recently, it has been shown that when G is quasi-transitive, there is another critical value pu ∈ [pc, 1] such that the number of infinite clusters is a.s. ∞ for p ∈ (pc, pu), and a.s. one for p> pu. We prove a simultaneous version of this result in the canonical coupling of the percolation processes for all p ∈ [0, 1]. Simultaneously for all p ∈ (pc, pu), we also prove that each infinite cluster has uncountably many ends. For p> pc we prove that all infinite clusters are indistinguishable by robust properties. Under the additional assumption that G is unimodular, we prove that a.s. for all p1 < p2 in (pc, pu), every in-finite cluster at level p2 contains infinitely many infinite clusters at level p1. We also show that any Cartesian product G of d infinite connected graphs of bounded degree satisfies pu(G) ≤ pc(Z d).

Phase transition for parking blocks, Brownian excursion and coalescence

by P. Chassaing, G. Louchard , 2005
"... ..."
Abstract - Cited by 36 (4 self) - Add to MetaCart
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...gs) ; where n converges weakly to an extreme-value distribution. This paper is concerned with what we would call the "emergence of a giant block", by reference to the emergence of a giant c=-=omponent [4, 9, 14, 22, 28-=-]. We have: Theorem 1.1 For n and m going jointly to +1 (i) if p n = o(`), B n;` 1 =n P ! 0; 2 (ii) if ` = o( p n), B n;` 1 =n P ! 1. Thus a phase transition occurs for ` = ( p n). The main result of ...

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