On the Flexibility of Metropolis–Hastings Acceptance Probabilities in Auxiliary Variable Proposal Generation

Abstract. Use of auxiliary variables for generating proposal variables within a Metropolis–Hastings setting has been suggested in many different settings. This has in particular been of interest for simulation from complex distributions such as multimodal distributions or in transdimensional approaches. For many of these approaches, the acceptance probabilities that are used turn up somewhat magic and different proofs for their validity have been given in each case. In this article, we will present a general framework for construction of acceptance probabilities in auxiliary variable proposal generation. In addition to showing the similarities between many of the proposed algorithms in the literature, the framework also demonstrates that there is a great flexibility in how to construct acceptance probabilities. With this flexibility, alternative acceptance probabilities are suggested. Some numerical experiments are also reported.     

二元对立在《远大前程》中的体现

探讨《远大前程》中的三组主要的二元对立,分析作者对人性本真的肯定和对不切实际的幻想的否定,同时也通过二元对立的分析揭示了大英帝国的罪恶和对海外殖民地的掠夺的事实。     

A comparison of nonparametric priors in hierarchical mixture modelling for AFT regression

We will pursue a Bayesian nonparametric approach in the hierarchical mixture modelling of lifetime data in two situations: density estimation, when the distribution is a mixture of parametric densities with a nonparametric mixing measure, and accelerated failure time (AFT) regression modelling, when the same type of mixture is used for the distribution of the error term. The Dirichlet process is a popular choice for the mixing measure, yielding a Dirichlet process mixture model for the error; as an alternative, we also allow the mixing measure to be equal to a normalized inverse-Gaussian prior, built from normalized inverse-Gaussian finite dimensional distributions, as recently proposed in the literature. Markov chain Monte Carlo techniques will be used to estimate the predictive distribution of the survival time, along with the posterior distribution of the regression parameters. A comparison between the two models will be carried out on the grounds of their predictive power and their ability to identify the number of components in a given mixture density.     

粗集与神经网络相结合的股票价格预测模型

粗集和神经网络结合反映了人类智能的定性和定量、清晰和隐含、串行和并行相互交叉混合的常规思维机理。本文建立这样一种混合杂交模型用于股票价格波动趋势的预测,通过粗集对数据的二维约简预处理消除了样本中的噪声和冗余,在提高神经网络预测精度的同时降低了学习负担。为了获得最优的预测精度,本文还利用遗传算法进行属性离散化和网络学习。通过对上证综指的实证研究表明,这种混合杂交模型的性能明显优于BP和GA神经网络模型。     

证券市场流动性与交易者群体变动的混沌研究

从证券市场微观结构角度,利用交易者群体可变的动态模型,分别在确定与不确定性金 融环境下,研究离散交易状态下市场的形成过程,并分析了交易者群体变动的混沌条件. 结果 表明,市场流动性的最低标准是买卖双方的交易者群体要有一个恰当的比例,市场达到稳定流 动性的时间与描述离开股市交易者的参数有关,通过交易制度对交易者群体参数的影响,可实 现对市场过程的控制,最后给出仿真计算.     

The Steiner Tree Problem in Kalmanson Matrices and in Circulant Matrices

We investigate the computational complexity of two special cases of the Steiner tree problem where the distance matrix is a Kalmanson matrix or a circulant matrix, respectively. For Kalmanson matrices we develop an efficient polynomial time algorithm that is based on dynamic programming. For circulant matrices we give an -hardness proof and thus establish computational intractability.     

Semi-online scheduling with “end of sequence” information

We study a variant of classical scheduling, which is called scheduling with “end of sequence” information. It is known in advance that the last job has the longest processing time. Moreover, the last job is marked, and thus it is known for every new job whether it is the final job of the sequence. We explore this model on two uniformly related machines, that is, two machines with possibly different speeds. Two objectives are considered, maximizing the minimum completion time and minimizing the maximum completion time (makespan). Let s be the speed ratio between the two machines, we consider the competitive ratios which are possible to achieve for the two problems as functions of s. We present algorithms for different values of s and lower bounds on the competitive ratio. The proposed algorithms are best possible for a wide range of values of s. For the overall competitive ratio, we show tight bounds of ϕ + 1 ≈ 2.618 for the first problem, and upper and lower bounds of 1.5 and 1.46557 for the second problem. The authors would like to dedicate this paper to the memory of our colleague and friend Yong He who passed away in August 2005 after struggling with illness. D. Ye: Research was supported in part by NSFC (10601048).     

城市“外来蓝领”的就业与社会融合

利用中国人民大学人口与发展研究中心2005年7月在广东省惠州市的调查数据,以数家企业中的"外来蓝领"(即工厂、企业中的外来务工人员)为样本,对这些在企业中工作的外来务工人员的就业状况和社会融合进行分析。结果表明,这些"外来蓝领"对务工地的选择并非是盲目的,他们外出的动机开始呈现多元化趋势;随着国家一系列关于农民工培训和保障政策的出台,接受调查的大多数"外来蓝领"与用人单位之间签订了劳动合同,并对合同的内容有一定的了解,然而他们对政府的法规依赖性高,并且由于户口的限制,他们的社会保险在实施层面还存在较大的不足;尽管"外来蓝领"在与社区融合方面表现出较强的能力,但是同城市当地人之间的交流缺乏,造成其社会融合程度低,形成了"蚁巢"现象。     

Clustering and combinatorial optimization in recursive supervised learning

The use of combinations of weak learners to learn a dataset has been shown to be better than the use of a single strong learner. In fact, the idea is so successful that boosting, an algorithm combining several weak learners for supervised learning, has been considered to be the best off the shelf classifier. However, some problems still exist, including determining the optimal number of weak learners and the over fitting of data. In an earlier work, we developed the RPHP algorithm which solves both these problems by using a combination of global search, weak learning and pattern distribution. In this chapter, we revise the global search component by replacing it with a cluster based combinatorial optimization. Patterns are clustered according to the output space of the problem, i.e., natural clusters are formed based on patterns belonging to each class. A combinatorial optimization problem is therefore created, which is solved using evolutionary algorithms. The evolutionary algorithms identify the “easy” and the “difficult” clusters in the system. The removal of the easy patterns then gives way to the focused learning of the more complicated patterns. The problem therefore becomes recursively simpler. Over fitting is overcome by using a set of validation patterns along with a pattern distributor. An algorithm is also proposed to use the pattern distributor to determine the optimal number of recursions and hence the optimal number of weak learners for the problem. Empirical studies show generally good performance when compared to other state of the art methods.     

逆向工程特征参数提取技术研究

逆向工程是从已有实物的测量数据点中提取其特征参数进行模型重建的过程,特征参数的提取是逆向工程的关键技术之一。该文提出了一种新的回转面特征参数提取算法,将蚁群算法应用在逆向工程的特征参数优化过程中,首先建立回转面特征参数提取数学模型,然后运用蚁群算法实现过程优化,从而提取出特征参数,实现回转面图形重建,最后通过实例验证了该算法的有效性,该算法提高了特征参数提取精度和效率,适用于空间任意位置回转面的特征参数提取。