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Gibbs distributions for random partitions generated by a fragmentation process

机译:由分段过程生成的随机分区的吉布斯分布

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In this paper we study random partitions of {1,...,n} where every cluster of size j can be in any of w (j) possible internal states. The Gibbs (n,k,w) distribution is obtained by sampling uniformly among such partitions with k clusters. We provide conditions on the weight sequence w allowing construction of a partition valued random process where at step k the state has the Gibbs (n,k,w) distribution, so the partition is subject to irreversible fragmentation as time evolves. For a particular one-parameter family of weight sequences w (j) , the time-reversed process is the discrete Marcus-Lushnikov coalescent process with affine collision rate K (i,j) = a+b(i+j) for some real numbers a and b. Under further restrictions on a and b, the fragmentation process can be realized by conditioning a Galton-Watson tree with suitable offspring distribution to have n nodes, and cutting the edges of this tree by random sampling of edges without replacement, to partition the tree into a collection of subtrees. Suitable offspring distributions include the binomial, negative binomial and Poisson distributions.
机译:在本文中,我们研究{1,...,n}的随机分区,其中大小为j的每个群集都可以处于w(j)个可能的内部状态中的任何一个。 Gibbs(n,k,w)分布是通过在具有k个簇的此类分区之间进行均匀采样而获得的。我们在权重序列w上提供条件,以允许构造分区值随机过程,其中在步骤k,状态具有吉布斯(n,k,w)分布,因此随着时间的发展,分区会遭受不可逆的分裂。对于权重序列w(j)的特定一参数族,时间反转过程是离散Marcus-Lushnikov合并过程,对于某些实数,仿射碰撞率K(i,j)= a + b(i + j)数字a和b。在对a和b的进一步限制下,可以通过将具有合适后代分布的高尔顿-沃森树调整为具有n个节点,并通过对边缘进行随机采样而无需替换来切割该树的边缘,将树划分为子树的集合。合适的后代分布包括二项式,负二项式和泊松分布。

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