Bayesian validation assessment of multivariate computational models

Multivariate model validation is a complex decision-making problem involving comparison of multiple correlated quantities, based upon the available information and prior knowledge. This paper presents a Bayesian risk-based decision method for validation assessment of multivariate predictive models under uncertainty. A generalized likelihood ratio is derived as a quantitative validation metric based on Bayes’ theorem and Gaussian distribution assumption of errors between validation data and model prediction. The multivariate model is then assessed based on the comparison of the likelihood ratio with a Bayesian decision threshold, a function of the decision costs and prior of each hypothesis. The probability density function of the likelihood ratio is constructed using the statistics of multiple response quantities and Monte Carlo simulation. The proposed methodology is implemented in the validation of a transient heat conduction model, using a multivariate data set from experiments. The Bayesian methodology provides a quantitative approach to facilitate rational decisions in multivariate model assessment under uncertainty.     

Ranking Nuclear and Radiological Terrorism Scenarios: The Italian Case

A quantitative criterion for ranking the different scenarios of nuclear and radiological terrorism has been developed. The aim of the model is not to predict terroristic events but only to indicate which scenario has the higher utility from the point of view of a terroristic organization in terms of balance between factors favoring and discouraging the attack, respectively. All these factors were quantified according to a scoring system that takes into account the logarithmic relationship between perceptions and stimuli. The criterion was applied to several scenarios, each of which was modeled in a simple but not trivial way in order to estimate the expected damage in terms of probable life losses from both radiative and nonradiative effects. The outcome from the ranking method indicates that the attractive scenario appears to be the detonation of a low yield improvised nuclear device in the metropolitan area of a major city.     

高校内部审计风险原因及对策研究

内部审计机构的相对独立性、审计人员业务能力的局限性、审计方法及审计手段的落后状况等因素决定了内部审计风险的客观存在,目前高校办学和业务活动出现了一些新的特点,高校的内部审计工作不仅同样也存在风险,且较之以前有加大的趋势。本文论述了高校内部审计风险产生原因并相应提出了防范和控制风险的措施。     

从会计角度看中国企业应对反倾销的对策

我国是目前国际上遭受反倾销调查最多的国家,国外反倾销给我国出口贸易造成了巨大损失。会计是反倾销诉讼中的一个关键因素。文章针对会计在我国企业应对反倾销中的作用和我国企业应对反倾销中存在的会计问题进行了分析,在此基础上提出了我国企业从会计角度应对反倾销的对策与建议。     

法国会计模式的分析及对中国的借鉴

该文分析了法国会计模式的特点和影响因素 ,认为法国会计模式与中国会计模式存在相似之处 ,并借鉴法国 1 996年以来会计改革的经验 ,对完善我国会计模式提出了建议     

论会计准则的实施与激励性管制

介绍了欧洲会计准则实施的经验和做法 ,归纳中国转轨经济下会计准则实施现状 ,依据会计准则实施激励的理论基础 ,提出会计准则实施激励性管制这一创新思路 ,最后给出若干启示与建议     

Loss functions for estimation of extrema with an application to disease mapping

It is often of interest to find the maximum or near maxima among a set of vector‐valued parameters in a statistical model; in the case of disease mapping, for example, these correspond to relative‐risk “hotspots” where public‐health intervention may be needed. The general problem is one of estimating nonlinear functions of the ensemble of relative risks, but biased estimates result if posterior means are simply substituted into these nonlinear functions. The authors obtain better estimates of extrema from a new, weighted ranks squared error loss function. The derivation of these Bayes estimators assumes a hidden‐Markov random‐field model for relative risks, and their behaviour is illustrated with real and simulated data.     

对建立我国会计诚信体系的思考

近年来会计造假问题已成为一个世界性的难题。加快建立适应社会主义市场经济体制的会计诚信体系,营造良好的会计信用环境,已成为我国目前亟待解决的重大问题。从会计诚信法规、监督和会计诚信教育等方面探讨如何建立我国的会计诚信体系。     

构建我国法务会计人才培养模式

随着我国市场经济的快速发展，经济纠纷、经济案件中所涉及的财务会计问题越来越多，迫切需要法务会计人员从会计和法律的专业角度进行调查、取证和鉴定，为法庭提供专家意见。但我国法务会计理论研究滞后、从业人员素质不高、法务会计人才严重匮乏，远远不能满足日益增长的法务会计需求。由于法务会计与财务会计有较大区别，因此，我国应加强法务会计建设，强化法务会计理论研究，并针对我国法务会计的现状，构建学历教育、在职教育和社会教育三位一体的法务会计人才培养模式，以培养具有中国特色的、多层次的、复合型法务会计人才。     

Modeling External Risks in Project Management

To ascertain the viability of a project, undertake resource allocation, take part in bidding processes, and other related decisions, modern project management requires forecasting techniques for cost, duration, and performance of a project, not only under normal circumstances, but also under external events that might abruptly change the status quo. We provide a Bayesian framework that provides a global forecast of a project's performance. We aim at predicting the probabilities and impacts of a set of potential scenarios caused by combinations of disruptive events, and using this information to deal with project management issues. To introduce the methodology, we focus on a project's cost, but the ideas equally apply to project duration or performance forecasting. We illustrate our approach with an example based on a real case study involving estimation of the uncertainty in project cost while bidding for a contract.