运用心理学 讲好网络课

研究如何将心理学认知学习论运用在教学中. 强调新知识必须在学生的认知结构中找到固定点. 在分析 <计算机网络> 课程知识点的逻辑结构的基础上, 介绍了将新知识植入学生认知结构的讲授方法. 并探讨了几种固定新知识的链条和扫除学生认知结构断裂点的途径.     

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.     

BATHTUB-TYPE CURVES IN RELIABILITY AND BEYOND

Finding optimal, or at least good, maintenance and repair policies is crucial in reliability engineering. Likewise, describing life phases of human mortality is important when determining social policy or insurance premiums. In these tasks, one searches for distributions to fit data and then makes inferences about the population(s). In the present paper, we focus on bathtub‐type distributions and provide a view of certain problems, methods and solutions, and a few challenges, that can be encountered in reliability engineering, survival analysis, demography and actuarial science.     

谈分级显性意义假说对反语中的认知参照点的解释力

本文运用Giora的分级显性意义假说分析了Langacker的认知参照点理论，并对反语中的认知参照点进行分析，得出反语中的认知参照点也是层级突显的结论，为反语的理解提供一个新的视角。     

Causal Reasoning from Longitudinal Data*

Abstract. This paper reviews some of the key statistical ideas that are encountered when trying to find empirical support to causal interpretations and conclusions, by applying statistical methods on experimental or observational longitudinal data. In such data, typically a collection of individuals are followed over time, then each one has registered a sequence of covariate measurements along with values of control variables that in the analysis are to be interpreted as causes, and finally the individual outcomes or responses are reported. Particular attention is given to the potentially important problem of confounding. We provide conditions under which, at least in principle, unconfounded estimation of the causal effects can be accomplished. Our approach for dealing with causal problems is entirely probabilistic, and we apply Bayesian ideas and techniques to deal with the corresponding statistical inference. In particular, we use the general framework of marked point processes for setting up the probability models, and consider posterior predictive distributions as providing the natural summary measures for assessing the causal effects. We also draw connections to relevant recent work in this area, notably to Judea Pearl's formulations based on graphical models and his calculus of so‐called do‐probabilities. Two examples illustrating different aspects of causal reasoning are discussed in detail.     

Shrinkage confidence intervals for the normal mean: Using a guess for greater efficiency

If the unknown mean of a univariate population is sufficiently close to the value of an initial guess then an appropriate shrinkage estimator has smaller average squared error than the sample mean. This principle has been known for some time, but it does not appear to have found extension to problems of interval estimation. The author presents valid two‐sided 95% and 99% “shrinkage” confidence intervals for the mean of a normal distribution. These intervals are narrower than the usual interval based on the Student distribution when the population mean lies in such an “effective interval.” A reduction of 20% in the mean width of the interval is possible when the population mean is sufficiently close to the value of the guess. The author also describes a modification to existing shrinkage point estimators of the general univariate mean that enables the effective interval to be enlarged.     

而今迈步从头越--写在《云南民族大学学报》(哲学社会科学版)创刊20周年之际

《云南民族大学学报》走过了20年的发展历程。正值20岁生日时,随着学校的更名,学报有了新的名称,赋予了新的意义:一流的大学需要一流的学报。认真总结20年的办刊经验,坚持刊物的特色和品位,以更名为契机,努力开创学报工作的新局面,这是时代赋予我们的使命。     

增算子不动点定理及其应用

本文建立了若干新的增算子不动点存在定理,作为应用,作者研究了在Banach空间中一类含间断项Volterra积分方程解的存在性,我们的结果改进和推广了诸多已知结果。     

Summary of the Workshop on Issues in Risk Assessment: Quantitative Methods for Developmental Toxicology

This report summarizes the proceedings of a conference on quantitative methods for assessing the risks of developmental toxicants. The conference was planned by a subcommittee of the National Research Council's Committee on Risk Assessment Methodology ⁴ in conjunction with staff from several federal agencies, including the U.S. Environmental Protection Agency, U.S. Food and Drug Administration, U.S. Consumer Products Safety Commission, and Health and Welfare Canada. Issues discussed at the workshop included computerized techniques for hazard identification, use of human and animal data for defining risks in a clinical setting, relationships between end points in developmental toxicity testing, reference dose calculations for developmental toxicology, analysis of quantitative dose-response data, mechanisms of developmental toxicity, physiologically based pharmacokinetic models, and structure-activity relationships. Although a formal consensus was not sought, many participants favored the evolution of quantitative techniques for developmental toxicology risk assessment, including the replacement of lowest observed adverse effect levels (LOAELs) and no observed adverse effect levels (NOAELs) with the benchmark dose methodology.     

问题:科学创新的起点

从问题的角度出发 ,提出了问题是科学创新的起点 ,并进一步阐述了问题的来源和问题的意义。只要我们善于从问题出发 ,立足于问题 ,我们的科学创新就会有一番新天地。