基于拥挤收费的动态出发时间选择

针对动态交通分配领域中往往假定所有车辆的出发时间是固定的不正确假设，探讨基于拥挤收费的出发时间选择问题的负效用函数，建立动态出发时间选择问题的变分不等式模型．结果表明，建立的变分不等式模型等价于动态用户最优出发时间选择问题.     

考虑零售商质量控制和风险规避的供应链网络均衡研究

研究零售商质量控制和供应链成员风险规避背景下供应链网络均衡问题。应用条件风险值度量风险规避程度,利用变分不等式理论描绘供应链参与者的最优经济行为,设计供应链网络均衡模型,考虑风险规避和质量控制策略对均衡的影响,证明均衡解存在且唯一,并运用Levenberg-Marquardt算法求解均衡模型。最后对重要参数进行数值分析,揭示质量控制和风险规避对供应链均衡解以及参与者条件风险值的影响。研究表明：供应链参与者越规避风险越有利于产品质量水平的提高,风险规避程度的加深会降低制造商或零售商自身的CVaR,同时增大对方的CVaR,对因产品合格率提高带来的利益而言,制造商所得优于零售商。     

一类新的广义变分包含系统及其迭代算法

在Banach空间中介绍了一类新的广义变分包含系统.利用松弛增生映象及预解算子,证明了该广义变分包含系统解的存在性和唯一性,同时也建立了收敛于该唯一解的迭代算法,并给出相应的迭代收敛定理.     

住房保障体系中的廉租房退出机制

在政府构筑的保障房体系中,廉租房是最基础的层次,但由于廉租房在制度上缺乏有效的退出机制,准入制度的设计也存在缺陷,再加上整个住房保障体系层次间流转不畅,我国各大城市廉租房普遍存在"只进不出"现象。因此。从整体来看,政府需要完善准入制度,构建动态监管的廉租房退出机制,并完善多层次阶梯式的住房保障体系。     

SEMIPARAMETRIC REGRESSION AND GRAPHICAL MODELS

Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes , facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology.     

VARIATIONAL BAYESIAN ANALYSIS FOR HIDDEN MARKOV MODELS

The variational approach to Bayesian inference enables simultaneous estimation of model parameters and model complexity. An interesting feature of this approach is that it also leads to an automatic choice of model complexity. Empirical results from the analysis of hidden Markov models with Gaussian observation densities illustrate this. If the variational algorithm is initialized with a large number of hidden states, redundant states are eliminated as the method converges to a solution, thereby leading to a selection of the number of hidden states. In addition, through the use of a variational approximation, the deviance information criterion for Bayesian model selection can be extended to the hidden Markov model framework. Calculation of the deviance information criterion provides a further tool for model selection, which can be used in conjunction with the variational approach.     

Climate regime shift detection with a trans‐dimensional,sequential Monte Carlo,variational Bayes method

We present an application study which exemplifies a cutting edge statistical approach for detecting climate regime shifts. The algorithm uses Bayesian computational techniques that make time‐efficient analysis of large volumes of climate data possible. Output includes probabilistic estimates of the number and duration of regimes, the number and probability distribution of hidden states, and the probability of a regime shift in any year of the time series. Analysis of the Pacific Decadal Oscillation (PDO) index is provided as an example. Two states are detected: one is associated with positive values of the PDO and presents lower interannual variability, while the other corresponds to negative values of the PDO and greater variability. We compare this approach with existing alternatives from the literature and highlight the potential for ours to unlock features hidden in climate data.     

GENERALIZED EXTREME VALUE ADDITIVE MODEL ANALYSIS VIA MEAN FIELD VARIATIONAL BAYES

We develop Mean Field Variational Bayes methodology for fast approximate inference in Bayesian Generalized Extreme Value additive model analysis. Such models are useful for flexibly assessing the impact of continuous predictor variables on sample extremes. The new methodology allows large Bayesian models to be fitted and assessed without the significant computing costs of Markov Chain Monte Carlo methods. We illustrate our new methodology with maximum rainfall data from the Sydney, Australia, hinterland. Comparisons are made between the Mean Field Variational Bayes and Markov Chain Monte Carlo approaches.     

考虑技术投资的供应链网络下可交易污染排放许可均衡问题

构建了包含多污染物、多污染接收点的供应链网络,考虑了生产技术和减排技术投资,对可交易的污染排放许可下的供应链网络进行了均衡分析,给出了变分不等式框架,建立了均衡模型。模型明确地处理了空间差异,同时保证了通过初始污染排放许可分配就可达到指定的环境标准。提出了模型的求解算法,最后利用提出的模型及算法对算例进行了计算,得到利润最大化下供应链网络中各工厂的产品生产量、排放量、污染排放许可的均衡分配额和污染排放许可的交易价格、技术最优投资额,并分析了成本函数参数以及工厂数量的变化对生产技术投资和减排技术投资的影响。     

基于连续时间动态博弈的供应链声誉及其微分对策研究

着眼于企业及其供应链的可持续发展,纳入时间因素分析供应链协调问题.基于供应链视角考虑顾客价值开拓,并将其本质抽象为“供应链声誉”的构建过程.针对这一供应链动态协调问题,基于供应链成员企业之间的长期合作关系,在连续时间背景下采用变分法建立了供应链各方关于供应链声誉的微分对策模型,通过求解该模型发现在拟约束方程组有解时Nash均衡存在,并求出了相应的最优微分对策.这些理论研究结论阐明了长期的、稳定的合作关系对于实现供应链协调的意义,对于实践中供应链动态协调的实现具有一定的启发与参考价值.论文最后对相关结论进行了数值分析.