一种有向图并行性拓扑排序算法

论述了有向图并行性拓扑排序的基本概念及其算法,解决了目前有向图拓扑序列串行性的缺点,说明有向图并行拓扑排序具有应用范围广、实用价值大的特点.     

单顶边星图的优美性

在齿轮图的每个齿的齿顶分别加上一条悬挂边后构成的图称为单顶边星图，本文证明了单顶边星图是优美图，补充了文（２）的结果，从而彻底解决了文（１）的猜想。     

简支吊车梁的活载弯矩包络图

论述了在吊车荷载作用下的简支吊车梁的弯矩包络图的分段、方程求法,最后通过实例说明包络图的绘制。     

线图连通度的界

首先给出了线图连通度κ_L的一个上界;κ_L≤δ+△-2;其次得出了在条件δ≥[n/2]+1下κ_L的一个很好的下界;κ_L≥2δ-2;由此得到当δ≥[n/2]+1时,若G为正则图,则κ_L=2δ-2,若G为拟正则图,则κ_L=2δ-2或2δ-1。     

两类图的和数

研究了两类完全多部图的和数,证明了图K1,1,r和K1,1,1,r(r≥3)的和数分别是r和r 2.     

Researches on the Distribution Domains of G(4) G(4)的分布域之研究

讨论了有固定的最大度△=4和n个顶点的一类无自环,无向、连通平面图G(△)的异构类的分布域R(v,n)(圈秩v=1---n+1)的三种情况,从而获得R(v,n)的A型、B型和C型的分布定理,并举例说明分布域在图论和碳氢化合物上的应用。     

Ancestral Inference in Population Genetics Models with Selection (with Discussion)

A new algorithm is presented for exact simulation from the conditional distribution of the genealogical history of a sample, given the composition of the sample, for population genetics models with general diploid selection. The method applies to the usual diffusion approximation of evolution at a single locus, in a randomly mating population of constant size, for mutation models in which the distribution of the type of a mutant does not depend on the type of the progenitor allele; this includes any model with only two alleles. The new method is applied to ancestral inference for the two‐allele case, both with genic selection and heterozygote advantage and disadvantage, where one of the alleles is assumed to have resulted from a unique mutation event. The paper describes how the method could be used for inference when data are also available at neutral markers linked to the locus under selection. It also informally describes and constructs the non‐neutral Fleming–Viot measure‐valued diffusion.     

Graphical models for skew-normal variates

This paper explores the usefulness of the multivariate skew-normal distribution in the context of graphical models. A slight extension of the family recently discussed by Azzalini & Dalla Valle (1996 ) and Azzalini & Capitanio (1999 ) is described, the main motivation being the additional property of closure under conditioning. After considerations of the main probabilistic features, the focus of the paper is on the construction of conditional independence graphs for skew-normal variables. Necessary and sufficient conditions for conditional independence are stated, and the admissible structures of a graph under restriction on univariate marginal distribution are studied. Finally, parameter estimation is considered. It is shown how the factorization of the likelihood function according to a graph can be rearranged in order to obtain a parameter based factorization.     

Bayesian phylogenetic inference from animal mitochondrial genome arrangements

Summary. The determination of evolutionary relationships is a fundamental problem in evolutionary biology. Genome arrangement data are potentially more informative than deoxyribonucleic acid sequence data for inferring evolutionary relationships between distantly related taxa. We describe a Bayesian framework for phylogenetic inference from mitochondrial genome arrangement data using Markov chain Monte Carlo methods. We apply the method to assess evolutionary relationships between eight animal phyla.     

Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property

Abstract.  The Andersson–Madigan–Perlman (AMP) Markov property is a recently proposed alternative Markov property (AMP) for chain graphs. In the case of continuous variables with a joint multivariate Gaussian distribution, it is the AMP rather than the earlier introduced Lauritzen–Wermuth–Frydenberg Markov property that is coherent with data-generation by natural block-recursive regressions. In this paper, we show that maximum likelihood estimates in Gaussian AMP chain graph models can be obtained by combining generalized least squares and iterative proportional fitting to an iterative algorithm. In an appendix, we give useful convergence results for iterative partial maximization algorithms that apply in particular to the described algorithm.