高校收费的财政理论分析

高校收费是我国高校改革的重要内容之一,理论界对此争论比较激烈,说法不一。作者以财政理论为基础,首先比较税收与收费的异同,从不同社会产品具有不同价值补偿方式入手,深入分析高等教育作为一种社会产品所适应的补偿方式,从而找到高校收费的理论支持,并针对现行高校收费存在的问题进行分析,同时提出政策建议。     

河南农业经济结构调整对农业劳动力的挑战

河南是农业大省,要使河南农业进一步发展,农民收入进一步增加,河南农业经济必须进行结构调整。农业经济结构调整的主体是农业劳动力,而农业劳动力素质,特别是农业劳动力文化素质的高低则成为河南农业经济结构调整的关键。文中重点分析了河南省农业劳动力文化素质的现状及与农业经济结构调整要求的差距,最后提出了提高河南省农业劳动力文化素质的对策。     

汉代边郡军粮廪给问题探讨

粮食供给是军队后勤给养中的一项重要内容。汉代边塞地区军粮主要供给吏卒及其家属,也用于借贷和马、牛、驼、狗之食。从量制、时制、身份差别、劳动强度和年龄、性别等方面,分析了军队吏卒和吏卒家属口粮分等廪给的具体情况及其影响要素,从而揭示出汉代军粮供给体制的内在规律。正是由于汉代军粮廪给有章可循,才最大限度地提高了后勤的供应能力和军队的战斗力,保证了军队平时守土卫边和战时有效打击来犯之敌。     

教育服务理论与学校管理理念转变的思考

以教育服务理论为依据,从经济学角度,阐述了教育服务是教育产品,学校是教育服务产品的生产者和供给者,学生是教育服务产品的消费者和需求者。指出学校应以经营的理念管理学校,以消费者(生源)为本,充分发挥学校教育服务的优势,向市场和消费者提供品牌和特色的优质的教育服务,以吸引更多的消费者。     

加入WTO后我国高等农业院校面临的形势与对策

加入WTO是中国农业全面与世界经济接轨的切入点。它为农业带来了机遇和挑战 ,也将对高等农业院校改革与发展产生重大影响。高等农业院校应正视加入WTO带来的影响 ,加快学校改革与发展 ,加大学科调整与重组力度 ,加快教育体制改革 ,加强国际间教学和科研交流与合作 ,积极开展科学研究 ,推动科技创新 ,努力培养创新型人才。     

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.     

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.