对工程造价控制管理问题的探讨

分析我国工程造价管理中存在的问题;从工程建设全过程出发,论述如何控制工程造价。     

论刑事审判的价值追求

我国刑事审判的价值追求应是,一要确保无罪的人不受刑事追究,二要打击和惩罚犯罪,三要实现罪刑均衡,四要教育感化犯罪人。这样就会更高层次地提高我国刑事法治的整体水平,影响刑事司法与整个社会经济发展、文明进步的协调一致,并最终促进当代社会最为关注的目标———人权保障目标的实现。     

基本剩余价值初探

剩余价值在其历史发展中相继地采取了绝对剩余价值、相对剩余价值和基本剩余价值的直接表现形式。基本剩余价值是在绝对剩余价值和相对剩余价值尚未产生或产而复失的历史场合中必须生产的 ,也是在实行绝对剩余价值生产和相对剩余价值生产的历史场合中 ,被它们凸现出来并使它们赖以产生的具有原先既定性 ,或扩张基础性和增生本体性的剩余价值形式。基本剩余价值生产与资本主义生产相始终 ,是资本主义生产目的的基本实现。它和绝对剩余价值、相对剩余价值在质和量及其历史形态上的差异 ,使剩余价值成为一个比较经济范畴     

Improving Convergence of the HastingsMetropolis Algorithm with an Adaptive Proposal

The HastingsMetropolis algorithm is a general MCMC method for sampling from a density known up to a constant. Geometric convergence of this algorithm has been proved under conditions relative to the instrumental (or proposal) distribution. We present an inhomogeneous HastingsMetropolis algorithm for which the proposal density approximates the target density, as the number of iterations increases. The proposal density at the n th step is a non-parametric estimate of the density of the algorithm, and uses an increasing number of i.i.d. copies of the Markov chain. The resulting algorithm converges (in n ) geometrically faster than a HastingsMetropolis algorithm with any fixed proposal distribution. The case of a strictly positive density with compact support is presented first, then an extension to more general densities is given. We conclude by proposing a practical way of implementation for the algorithm, and illustrate it over simulated examples.     

价值观的形成规律与当代青年价值观现状

价值观的形成有其自身的规律 ,并受到社会因素和个体内在因素的影响。当代青年正处在我国改革开放进一步深化和社会主义市场经济体制逐步确立及自我意识发展和自我需要扩大的时期 ,因此 ,他们的价值观必然打着当今时代的烙印 ,具有开放性、先锋化、情绪性等特点 ,他们追求高效、公正、互利 ,追求求职与成才相结合 ,但同时 ,其自身的身心失范问题也日趋严重。     

以课程建设为核心，加强素质教育

指出素质教育是一种“以人为本”的教育观 ,注重人的全面发展。强调课程建设是实施素质教育的基础性工作 ,对提高人才培养质量起到关键性的作用。以东北大学平台课程建设为例 ,分析了目前高等学校课程建设的现状 ,并提出课程建设的中心任务是更新教育观念 ,改革教学内容和课程体系 ,以适应学生创新能力培养的需要 ,为高等学校课程建设工作的改革与发展提供理论依据。     

基于贝叶斯神经网络的非参数回归

提高神经网络模型推广能力的关键是控制模型的复杂度。该文探索了贝叶斯神经网络的非参数回归的建模方法,通过融入模型参数的先验知识,在给定数据样本及模型假设下进行后验概率的贝叶斯推理,使用马尔可夫链蒙特卡罗算法来优化模型控制参数,实现了对神经网络模型中不同部分复杂度的控制,获得了模型参数的后验分布及预测分布。在5个含噪二维函数回归问题上的应用显示了模型的复杂度能根据数据的复杂度而自适应调整,并给出了较好的预测结果。     

基于马尔可夫链的产品市场保有量预测模型

售后服务机构为了准确地准备库存,都需要预测产品的市场保有量。基于马尔可夫链建立的状态概率矩阵模型,与抽样调查、时间序列法、回归分析法等方法相比,更能准确预测市场产品保有量。与传统产品相比较,更新换代速度快的电子产品,要预测其市场保有量,更需要利用状态概率矩阵模型。     

Estimating rating transition probabilites with missing data

In this article we provide a rigorous treatment of one of the central statistical issues of credit risk management. GivenK-1 rating categories, the rating of a corporate bond over a certain horizon may either stay the same or change to one of the remainingK-2 categories; in addition, it is usually the case that the rating of some bonds is withdrawn during the time interval considered in the analysis. When estimating transition probabilities, we have thus to consider aK-th category, called withdrawal, which contains (partially) missing data. We show how maximum likelihood estimation can be performed in this setup; whereas in discrete time our solution gives rigorous support to a solution often used in applications, in continuous time the maximum likelihood estimator of the transition matrix computed by means of the EM algorithm represents a significant improvement over existing methods.     

On Block Updating in Markov Random Field Models for Disease Mapping

Gaussian Markov random field (GMRF) models are commonly used to model spatial correlation in disease mapping applications. For Bayesian inference by MCMC, so far mainly single-site updating algorithms have been considered. However, convergence and mixing properties of such algorithms can be extremely poor due to strong dependencies of parameters in the posterior distribution. In this paper, we propose various block sampling algorithms in order to improve the MCMC performance. The methodology is rather general, allows for non-standard full conditionals, and can be applied in a modular fashion in a large number of different scenarios. For illustration we consider three different applications: two formulations for spatial modelling of a single disease (with and without additional unstructured parameters respectively), and one formulation for the joint analysis of two diseases. The results indicate that the largest benefits are obtained if parameters and the corresponding hyperparameter are updated jointly in one large block. Implementation of such block algorithms is relatively easy using methods for fast sampling of Gaussian Markov random fields ( Rue, 2001 ). By comparison, Monte Carlo estimates based on single-site updating can be rather misleading, even for very long runs. Our results may have wider relevance for efficient MCMC simulation in hierarchical models with Markov random field components.