基于贝叶斯信息更新的动态库存与定价研究

考虑价格对需求量的扰动并利用贝叶斯公式对需求分布函数中的未知参数进行不断学习更新,研究缺货部分比例延迟交货情形下的动态库存与动态定价问题,刻画了最优利润函数的性质并证明了"基准库存列表价格"是最优的库存价格水平,并由此得到了最优的补货策略和定价策略。     

基于贝叶斯信息更新的失事飞机发现概率模型

了目标搜索区域的确定方法以及失事飞机在目标搜索区域的初始概率分布,得到发现概率的计算公式。以发现概率为目标,构造了一个求解最优搜寻策略的Max Max化规划模型,模型可以动基于贝叶斯方法,提出了一个失事飞机的发现概率模型,利用飞机失联前后的信息数据,给出态地对坠机点的概率分布进行更新,使下一步搜寻任务得到及时的修正和调整。考虑到洋流对坠机点的影响,本文还提出了一个关于基点先验概率分布的重构策略。此外,对任务搜索区域最优路径的选取问题做了进一步探讨,给出了一个任务搜索区域上搜寻路径的选取方法     

考虑消费者信息更新的概率销售策略

概率销售模式下,部分商家不披露其产品抽中的概率信息,消费者需通过产品在线评论等收集信息以更新对抽取概率的预估,从而产生信息更新行为。此外,概率销售往往导致抽中不满意产品的消费者将产品转售。综合考虑消费者的信息更新和产品转售行为,本文分别构建了信息披露和不披露情形下的概率销售企业策略优化模型,求解并对比了不同信息披露情形下的产品定价及零售商利润,总结了信息精度等重要因素对零售商的定价、信息披露策略以及利润的影响。结论表明,信息不披露会降低概率产品价格。此外,当消费者获得的信息精度较低或转售价格较高时,零售商倾向于不披露产品的抽取概率信息;反之,则披露信息更有利。     

贝叶斯库存管理方式下供应链中的信息汇聚效应

考虑由一个供应商和两个零售商组成的二级供应链,假设产品需求分布未知,零售商将观察到的需求信息共享给供应商,各企业按贝叶斯方法管理生产和库存。本文将表明在一定条件下,信息的汇聚可能会扭曲供应商对市场需求的认识,造成供应商严重产品积压或严重缺货,我们把这种现象称为信息汇聚效应。本文将给出需求信息可完全观察时,多周期供应链中发生信息汇聚效应的充分条件,并推出发生信息汇聚效应的概率的下界。     

更新风险模型和延迟更新风险模型中破产概率的若干结果

本文进一步研究更新风险模型和延迟更新风险模型中的破产概率ψ(χ),这里χ是保险公司的初始资本.在假定个体索赔分布是重尾的前提下,得到了与经典模型相一致的破产概率ψ(χ)的一个尾等价关系.     

更新风险模型和延迟更新风险模型中破产概率的若干结果

本文进一步研究更新风险模型和延迟更新风险模型中的破产概率ψ(x),这里x是保险公司的初始资本.在假定个体索赔分布是重尾的前提下,得到了与经典模型相一致的破产概率ψ(x)的一个尾等价关系.     

基于双边库存风险的供应链契约研究

市场需求的不确定性会在很大程度上影响供应链参与者的实际收益,在选择供应链契约的时候不仅要考虑利益的合理分配,还要考虑获得相应收益时决策者所承担的风险大小,以此来衡量市场需求变化可能会造成的损失.首先构建基于双边库存风险的供应链契约模型,对比常见供应链契约分析其参数对双边库存风险的分担情况,分析风险视角下的契约等价性;然后考虑双边库存风险产生的概率以及所造成的损失,分析决策者基于风险角度对常见供应链契约的选择;最后以供应链参与者分担相同的风险以及实现帕累托优化为目标,得到了基于双边库存风险的供应链契约协调条件,同时对比常见供应链契约得到其基于风险角度的等价条件.     

顾客策略行为下基于联合促销努力的风险规避供应链协调模型

本文在考虑顾客策略行为情形下,利用条件风险度量准则建立了带有联合促销努力供应链协调模型,研究了风险规避对销售商和供应商决策行为的影响,得到了如下研究结论:(1)集中决策情形,价格承诺策略既可以激励供应商提高生产量,还能够有效降低顾客策略行为对其产生的负面影响。(2)分散决策情形,销售商风险规避度增大,销售商的订购量将增加,供应商的销售努力也将增加;供应商风险规避度增大,销售商的订购量将减小,供应商的销售努力也将减小;无论供应商和销售商的风险规避如何变化,销售商的销售努力不变。(3)利用回购和成本分担组成的混合契约可以实现供应链完美协调。     

更新风险模型中破产概率的一个局部结果

进一步研究延迟更新风险模型,在假定个体索赔额是重尾分布的前提下得到了破产概率的一个局部等价式R(x,x z]～z/ρμ^-F(x),其中F表示索赔额的分布函数,μ为其均值,ρ表示模型的安全负荷系数,极限过程是x→∞．并且对Sparre Anderson模型作了推广,得到了相应的结果．     

复合更新风险模型下的破产概率

讨论了具有较一般意义的复合更新风险模型下的破产概率,在假定索赔分布属于重尾分布族的前提下,得到了我们所渴望的破产概率的尾等价形式.这一结果恰与经典的Cram啨r-Lundberg模型下的结论相一致.     

基于双边过度自信及风险厌恶的委托-代理问题研究

研究了委托人与代理人双边过度自信倾向及风险厌恶偏好情形下的委托-代理问题.结论表明最优风险分担水平随着委托人风险厌恶程度及代理人过度自信水平的增大而增加,随着代理人风险厌恶程度和委托人过度自信水平的减少而减少.最优努力水平随代理人风险厌恶程度及委托人过度自信水平的增大而提高,随委托人风险厌恶程度和代理人过度自信水平的减少而减少.     

Clinical and Operational Risk: A Bayesian Approach

In health care organizations (HCOs) adverse events may provoke dangerous consequences on patients, such as death, a longer hospital stay, and morbidity. As a consequence, HCO’s department needs to manage legal issues and economic reimbursements. Governances and physicians are interested in operational (OR) and clinical risk (CR) assessment, mainly for forecasting and managing losses and for a correct decision making. Currently, scientific researches, which are objected to a quantification of CR and OR in HCO, are scarce; absence of regulatory constraints and limited awareness of benefits due to risk management do not provide incentives to elaborate on how risks can be quantified. This paper is aimed at proposing Bayesian methods to manage operational and clinical adverse events in health care. Bayesian Networks (BNs) are useful for assessing risks given end stage renal disease (ESRD) as a context of application; some prior probability distributions are advised for representing knowledge before experimental results and Bayesian utility functions for making the optimal decision. The method is described as from the theoretical as from the empirical point of view, thanks to the health care and haemodialysis department, for this application. The ultimate goal is to introduce a methodology useful for managing operational and clinical risk for haemodialysis patients and departments.      

Two-product inventory management with fixed costs and supply uncertainty

This paper determines the optimal ordering policy for a two-product, periodic-review inventory problem in which the probability of supply availability is unknown. Moreover, there are two different fixed costs assigned to each product. Demand rates are random variables with known probability density functions, and the supply availability for each product is updated at the beginning of each time period. We prove the optimality of (s,S) policy with a monotone switching curve that indicates the priority of production, where the order-up-to levels and the reorder points are functions of supply availability information. A simple computation is proposed to calculate the two threshold levels. Bayesian updating helps to manage the optimal ordering policy by updating supply disruption information. Numerical results show that improving the accuracy of the forecast leads to making a better ordering decision and eliminating the negative effect of supply disruption on the total cost.     

Bayesian solution to pricing and inventory control under unknown demand distribution

This paper addresses the simultaneous determination of pricing and inventory control with learning. The Bayesian formulation of this model results in a dynamic program with a multi-dimension state-space. We show that the state-space of the Bayesian model can be reduced under some conditions and characterize the structure of the optimal policy.     

Quick response policy with Bayesian information updates

In this paper we investigate the quick response (QR) policy with different Bayesian models. Under QR policy, a retailer can collect market information from the sales of a pre-seasonal product whose demand is closely related to a seasonal product’s demand. This information is then used to update the distribution for the seasonal product’s demand by a Bayesian approach. We study two information update models: one with the revision of an unknown mean, and the other with the revision of both an unknown mean and an unknown variance. The impacts of the information updates under both models are compared and discussed. We also identify the features of the pre-seasonal product which can bring more significant profit improvement. We conclude that an effective QR policy depends on a precise information update model as well as a selection of an appropriate pre-seasonal product as the observation target.     

Bayesian decision-making in inventory modelling

The objective of this paper is to advocate the use of Bayesianmethods in tackling decision problems with limited past data.It is assumed that a Bayesian approach is least likely to besuccessful when there is no information on which to base a meaningfulprior. Here we use a limiting, invariant, form of the conjugateprior distribution to represent this ignorance. The resultsof decisions based on Bayesian methods with this ‘non-informative’prior are compared with those which result from deriving a pointestimate for the unknown parameter. The particular context consideredhere is that of a single-period inventory model with compoundPoisson demand made up of a known demand size distribution butan unknown demand rate. The demand rate is assumed to be highenough for a normal approximation to the compound Poisson distributionto be used, in which case it is possible to analyse the behaviourdirectly. An extension to the multi-period model with zero leadtime is considered briefly. The results lend support to theuse of Bayesian methods, with or without a meaningful prior,for which the analysis and computation are no more complex thanthose required by standard methods.     

贝叶斯方法在信用风险度量中的应用研究综述

贝叶斯方法可以有效的处理信用风险度量中常见的数据缺失问题,而且为科学使用专家意见等主观经验提供了有效途径,已被广泛应用于信用风险度量领域。本文从模型构建、估计方法及模型比较三个方面对应用贝叶斯方法度量信用风险的重要文献进行综述,重点关注信用风险的违约相关性和风险蔓延性等最新研究热点,为深入研究信用风险度量问题提供参考,并引起国内风险分析人员对贝叶斯方法的兴趣。     

基于双边风险厌恶及存在监督的委托-代理模型研究

研究了委托人与代理人双边风险厌恶及存在监督情形下的委托-代理问题.结论表明非对称信息下最优风险分担系数是委托人风险厌恶程度的递增函数,是代理人风险厌恶程度的递减函数,代理人努力水平是其风险厌恶程度的递减函数.监督措施的存在提高了对代理人的激励强度.     

Bayesian Estimation and Statistical Analysis of Risk Measurements

The Bayesian model are established for the VaR and related risk measurements. The relationship between VaR and other risk measurements including expect shortfall, tail condition expectation and conditional value at risk are discussed. Furthermore, the Bayesian estimates and Bayesian predictors of these risk measurement are derived. Thirdly, the consistency and asymptotic normality in the exponential risk model are proved. Finally, the numerical simulation method is used to verify the convergence rate under different sample sizes.