Bayesian analysis of directed graphs data with applications to social networks

Summary.  A fully Bayesian analysis of directed graphs, with particular emphasis on applica- tions in social networks, is explored. The model is capable of incorporating the effects of covariates, within and between block ties and multiple responses. Inference is straightforward by using software that is based on Markov chain Monte Carlo methods. Examples are provided which highlight the variety of data sets that can be entertained and the ease with which they can be analysed.     

时间如何前进——进化论随笔

中国古代"与时偕行"、"与时俱化"的观念,以取法天地四时的运行为基准,是一种四时循环论的变化观.我党提出的"与时俱进"思想的含义,是随着时代的前进而前进,和随着时代的变化而变化,体现了鲜明的时代创新精神,它的哲学基础是科学进化论.这一特征在语词上通过一个"进"字表达出来.虽然"与时俱进"和"与时俱化"只有一字之差,却体现了本质的超越.同时,这一表述通过对中国古代<易经>、<庄子>等经典的吸收,丰富了自身的思想内涵,也建立,了联接传统与现代的一道桥梁.当然,由于现代时间观念的一维性质,使得人们在批驳"直线进化论"的同时,易于陷入"时间进化论",而时间进化论实际上也是一种直线进化论,这也是需要警醒的.     

Bayesian analysis of elapsed times in continuous‐time Markov chains

The authors consider Bayesian analysis for continuous‐time Markov chain models based on a conditional reference prior. For such models, inference of the elapsed time between chain observations depends heavily on the rate of decay of the prior as the elapsed time increases. Moreover, improper priors on the elapsed time may lead to improper posterior distributions. In addition, an infinitesimal rate matrix also characterizes this class of models. Experts often have good prior knowledge about the parameters of this matrix. The authors show that the use of a proper prior for the rate matrix parameters together with the conditional reference prior for the elapsed time yields a proper posterior distribution. The authors also demonstrate that, when compared to analyses based on priors previously proposed in the literature, a Bayesian analysis on the elapsed time based on the conditional reference prior possesses better frequentist properties. The type of prior thus represents a better default prior choice for estimation software.     

中国古代和谐思想探析

纵观中国传统文化，就会发现古人对和谐的理解是多方面、多层次的，是一种大和谐：人与自然的和谐；人与人的和谐；人与自身的和谐，世界的和谐。关于和谐的本质、和谐的价值、以及如何能够达到和谐等方面，在中国传统和谐文化中有比较完善的思想论述。     

中国古代隐喻研究述论

中国古代的隐喻研究开始早、成果丰而具有鲜明的民族特色和文化品格:大量实例充分证明中国传统文化在隐喻言说上更加侧重“隐”,强调“暗示”;早在先秦时期中国古人就已经对隐喻的概念、原理、分类、功能、运行机制等进行了深入探索,提出了一系列富有价值的隐喻理论;中国古代的隐喻理论大致经历了一个由自发到自觉、由零散到系统的发展过程。     

管理育人的关键是高效服务与优化管理--建设既安身又安心的大学生公寓

结合高校学生公寓社会化管理的实际情况,探讨在高校后勤社会化管理进程中,如何更新观念,高效服务与优化管理并重,实现管理育人.     

Markov Properties for Acyclic Directed Mixed Graphs

We consider acyclic directed mixed graphs, in which directed edges ( x → y ) and bi-directed edges ( x ↔ y ) may occur. A simple extension of Pearl's d -separation criterion, called m -separation, is applied to these graphs. We introduce a local Markov property which is equivalent to the global property resulting from the m -separation criterion for arbitrary distributions.     

Bayesian inference for a discretely observed stochastic kinetic model

The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient.     

Conditional Likelihood Estimators for Hidden Markov Models and Stochastic Volatility Models

ABSTRACT.  This paper develops a new contrast process for parametric inference of general hidden Markov models, when the hidden chain has a non-compact state space. This contrast is based on the conditional likelihood approach, often used for ARCH-type models. We prove the strong consistency of the conditional likelihood estimators under appropriate conditions. The method is applied to the Kalman filter (for which this contrast and the exact likelihood lead to asymptotically equivalent estimators) and to the discretely observed stochastic volatility models.     

On an adaptive version of the Metropolis–Hastings algorithm with independent proposal distribution

In this paper, we present a general formulation of an algorithm, the adaptive independent chain (AIC), that was introduced in a special context in Gåsemyr et al . [ Methodol. Comput. Appl. Probab. 3 (2001)]. The algorithm aims at producing samples from a specific target distribution Π, and is an adaptive, non-Markovian version of the Metropolis–Hastings independent chain. A certain parametric class of possible proposal distributions is fixed, and the parameters of the proposal distribution are updated periodically on the basis of the recent history of the chain, thereby obtaining proposals that get ever closer to Π. We show that under certain conditions, the algorithm produces an exact sample from Π in a finite number of iterations, and hence that it converges to Π. We also present another adaptive algorithm, the componentwise adaptive independent chain (CAIC), which may be an alternative in particular in high dimensions. The CAIC may be regarded as an adaptive approximation to the Gibbs sampler updating parametric approximations to the conditionals of Π.