皮尔士的逻辑人生

皮尔士早年曾受严格的逻辑训练,坚持以逻辑学为学术目标,一生热情地追求逻辑学的改进和应用。他的逻辑思想具有广阔的视野,对于逻辑学的发展作出了杰出贡献。然而,他作为一位伟大逻辑学家,长期不被人理解,只是在近几年才重新得到发现和重视。     

Theory & Methods: Krige,Smooth, Both or Neither?

Both kriging and non-parametric regression smoothing can model a non-stationary regression function with spatially correlated errors. However comparisons have mainly been based on ordinary kriging and smoothing with uncorrelated errors. Ordinary kriging attributes smoothness of the response to spatial autocorrelation whereas non-parametric regression attributes trends to a smooth regression function. For spatial processes it is reasonable to suppose that the response is due to both trend and autocorrelation. This paper reviews methodology for non-parametric regression with autocorrelated errors which is a natural compromise between the two methods. Re-analysis of the one-dimensional stationary spatial data of Laslett (1994) and a clearly non-stationary time series demonstrates the rather surprising result that for these data, ordinary kriging outperforms more computationally intensive models including both universal kriging and correlated splines for spatial prediction. For estimating the regression function, non-parametric regression provides adaptive estimation, but the autocorrelation must be accounted for in selecting the smoothing parameter.     

Bayesian analysis of human movement curves

Summary.  We consider the Bayesian analysis of human movement data, where the subjects perform various reaching tasks. A set of markers is placed on each subject and a system of cameras records the three-dimensional Cartesian co-ordinates of the markers during the reaching movement. It is of interest to describe the mean and variability of the curves that are traced by the markers during one reaching movement, and to identify any differences due to covariates. We propose a methodology based on a hierarchical Bayesian model for the curves. An important part of the method is to obtain identifiable features of the movement so that different curves can be compared after temporal warping. We consider four landmarks and a set of equally spaced pseudolandmarks are located in between. We demonstrate that the algorithm works well in locating the landmarks, and shape analysis techniques are used to describe the posterior distribution of the mean curve. A feature of this type of data is that some parts of the movement data may be missing—the Bayesian methodology is easily adapted to cope with this situation.     

中学生抗挫折能力培养探析

知识技能的掌握,是人的智力因素和非智力因素共同参与的结果。重视非智力因素的培养,特别是学生抗挫折能力的培养,不但有利于人才的成长,更有利于体育课教学质量的提高。针对培养动机、谋求策略、方法论三个方面的探讨,阐明重视学生抗挫折能力培养的教育才是全面的教育。     

寻症求道开创校园文化新天地

校园文化对学生的全面发展有着重要的影响。努力建设职业学校校园文化，对实现其培养目标，全面推行素质教育，培养学生的创新精神和实践能力有着无法比拟和替代的作用。本文从当前职业学校在校园文化建设中存在的问题入手，寻求对策，对职业学校校园文化建设做出有益的探讨，以推进职业学校校园文化建设。     

论国有企业核心竞争力的主要问题及对策

当今经济全球化趋势使国内外市场竞争日益激烈,怎样进一步提高国有企业的核心竞争能力,有效应对严峻的挑战,已成为影响我国国民经济持续快速增长的重要课题。我们应当认真研究国有企业的发展战略,开发核心技术,强化内部管理,参与世界市场竞争,努力提高国有企业的核心竞争力。     

解除方程组中四个变量为一组线性约束条件的数学处理方法

应用有限元方法求解流体力学或其他工程枝术问题,通常所得线性代数方程组的系数矩阵具有对称性。当方程组中有任意4个待定变量具有线性约束时,则可使用本文提供的数学处理方法,使原先n阶对称的系数矩阵在解除上述形式的约束条件后,方程组的阶次不变,而且系数矩阵仍具有对称性质。并且以此编制了计算机程序。     

高等职业教育课程目标探析

高等职业教育课程目标是一定教育阶段的专业课程方案力图最终达到的标准或预期学习结果，是指导课程研制过程的准则，是学生学完某个专业课程后将要达到的或获得的知识、能力、态度等，是教育目的和培养目标在课程领域的具体化。高等职业教育课程内容选择的依据和课程评价的标准，影响和制约职业教育的日常教学活动。高等职业教育课程开发应体现这种追求与精神，课程目标的取向要采用行为目标、生成性目标和表现性目标多元整合的形式。高等职业教育课程目标的确定依据是学生成长、社会发展和职业发展的需要，通过活动分析法、工作分析法、目的手段法和职业分析法等方法建立高等职业教育课程目标。     

A tutorial on adaptive MCMC

We review adaptive Markov chain Monte Carlo algorithms (MCMC) as a mean to optimise their performance. Using simple toy examples we review their theoretical underpinnings, and in particular show why adaptive MCMC algorithms might fail when some fundamental properties are not satisfied. This leads to guidelines concerning the design of correct algorithms. We then review criteria and the useful framework of stochastic approximation, which allows one to systematically optimise generally used criteria, but also analyse the properties of adaptive MCMC algorithms. We then propose a series of novel adaptive algorithms which prove to be robust and reliable in practice. These algorithms are applied to artificial and high dimensional scenarios, but also to the classic mine disaster dataset inference problem.     

Bayesian inference and model comparison for asymmetric smooth transition heteroskedastic models

Inference, quantile forecasting and model comparison for an asymmetric double smooth transition heteroskedastic model is investigated. A Bayesian framework in employed and an adaptive Markov chain Monte Carlo scheme is designed. A mixture prior is proposed that alleviates the usual identifiability problem as the speed of transition parameter tends to zero, and an informative prior for this parameter is suggested, that allows for reliable inference and a proper posterior, despite the non-integrability of the likelihood function. A formal Bayesian posterior model comparison procedure is employed to compare the proposed model with its two limiting cases: the double threshold GARCH and symmetric ARX GARCH models. The proposed methods are illustrated using both simulated and international stock market return series. Some illustrations of the advantages of an adaptive sampling scheme for these models are also provided. Finally, Bayesian forecasting methods are employed in a Value-at-Risk study of the international return series. The results generally favour the proposed smooth transition model and highlight explosive and smooth nonlinear behaviour in financial markets.