Approximate importance sampling Monte Carlo for data assimilation

Importance sampling Monte Carlo offers powerful approaches to approximating Bayesian updating in sequential problems. Specific classes of such approaches are known as particle filters. These procedures rely on the simulation of samples or ensembles of the unknown quantities and the calculation of associated weights for the ensemble members. As time evolves and/or when applied in high-dimensional settings, such as those of interest in many data assimilation problems, these weights typically display undesirable features. The key difficulty involves a collapse toward approximate distributions concentrating virtually all of their probability on an implausibly few ensemble members.

After reviewing ensembling, Monte Carlo, importance sampling and particle filters, we present some approximations intended to moderate the problem of collapsing weights. The motivations for these suggestions are combinations of (i) the idea that key dynamical behavior in many systems actually takes place on a low dimensional manifold, and (ii) notions of statistical dimension reduction. We illustrate our suggestions in a problem of inference for ocean surface winds and atmospheric pressure. Real observational data are used.     

Adaptive modeling, adaptive data assimilation and adaptive sampling

For efficient progress, model properties and measurement needs can adapt to oceanic events and interactions as they occur. The combination of models and data via data assimilation can also be adaptive. These adaptive concepts are discussed and exemplified within the context of comprehensive real-time ocean observing and prediction systems. Novel adaptive modeling approaches based on simplified maximum likelihood principles are developed and applied to physical and physical–biogeochemical dynamics. In the regional examples shown, they allow the joint calibration of parameter values and model structures. Adaptable components of the Error Subspace Statistical Estimation (ESSE) system are reviewed and illustrated. Results indicate that error estimates, ensemble sizes, error subspace ranks, covariance tapering parameters and stochastic error models can be calibrated by such quantitative adaptation. New adaptive sampling approaches and schemes are outlined. Illustrations suggest that these adaptive schemes can be used in real time with the potential for most efficient sampling.     

随机需求下仓库容量有限的物流库存管理问题的研究

在随机需求下，本文研究当物流企业自有仓库容量限时的物流库存管理问题，给出了使库存成本期望最小的订货量．     

Methods of rapid orbit forecasting after maneuvers for geostationary satellites

A geostationary (GEO) satellite may serve as a navigation satellite, but there is a problem that maneuvers frequently occur and the forces are difficult to model. Based on the technique of determining satellite orbits by transfer, a predicted orbit with high accuracy may be achieved by the method of statistical orbit determination in case of no maneuver force. The predicted orbit will soon be invalid after the maneuver starts, and it takes a long time to get a valid orbit after the maneuver ends. In order to improve ephemeris usability, the method of rapid orbit forecasting after maneuvers is studied. First, GEO satellite movement is analyzed in case of maneuvers based on the observation from the orbit measurement system by transfer. Then when a GEO satellite is in the free status just after maneuvers, the short arc observation is used to forecast the orbit. It is assumed that the common system bias and biases of each station are constant, which can be obtained from orbit determination with long arc observations. In this way, only 6 orbit elements would be solved by the method of statistical orbit determination, and the ephemeris with high accuracy may be soon obtained. Actual orbit forecasting with short arc observation for SINOSAT-1 satellite shows that, with the tracking network available, the precision of the predicted orbit (RMS of O-C) can reach about 5 m with 15 min arc observation, and about 3 m with 30 min arc observation. Supported by the National High Technology Research and Development Program of China (Grant No. 2006AA12Z322), the National Basic Research Program of China (Grant No. 2007CB815503), and the West Light Program of Chinese Academy of Sciences (Grant No. 2007LH01)     

Forecasting cancellation rates for services booking revenue management using data mining

Revenue management (RM) enhances the revenues of a company by means of demand-management decisions. An RM system must take into account the possibility that a booking may be canceled, or that a booked customer may fail to show up at the time of service (no-show). We review the Passenger Name Record data mining based cancellation rate forecasting models proposed in the literature, which mainly address the no-show case. Using a real-world dataset, we illustrate how the set of relevant variables to describe cancellation behavior is very different in different stages of the booking horizon, which not only confirms the dynamic aspect of this problem, but will also help revenue managers better understand the drivers of cancellation. Finally, we examine the performance of the state-of-the-art data mining methods when applied to Passenger Name Record based cancellation rate forecasting.     

Forecasting voting behaviour using machine learning – Poland in transition

The aim of the paper is to apply some inductive learning method from examples (which gives explicit decision rules of “if-then” type) to forecast the voting behaviour of individual members of the Polish Parliament. Results obtained are both interesting and promising. This revised version was published online in June 2006 with corrections to the Cover Date.     

Style goods pricing with demand learning

For many industries (e.g., apparel retailing) managing demand through price adjustments is often the only tool left to companies once the replenishment decisions are made. A significant amount of uncertainty about the magnitude and price sensitivity of demand can be resolved using the early sales information. In this study, a Bayesian model is developed to summarize sales information and pricing history in an efficient way. This model is incorporated into a periodic pricing model to optimize revenues for a given stock of items over a finite horizon. A computational study is carried out in order to find out the circumstances under which learning is most beneficial. The model is extended to allow for replenishments within the season, in order to understand global sourcing decisions made by apparel retailers. Some of the findings are empirically validated using data from U.S. apparel industry.     

在预报更新的环境下具有两种配送方式的供应链模型

本文研究在预报更新环境下具有快、慢两种配送方式和需求预报更新的库存系统，为了得到更多关于费用参数和预报改进对最优定货量以及最优的平均费用的影响，我们考虑两个周期的情形．以动态规划为工具我们得到了系统的最优策略．对于需求预报服从均匀分布情形，本文得到了最优定货量和最优的平均总费用的精确表达式．我们通过一些数值例子来说明库存费用、罚金、需求的预报改进和预报误差对最优定货量和最优的     

期货价格预测——反向传播神经网络模型

利用神经网络的 BP算法 ,对较难解决的期货价格问题做了一些研究 ,获得了一定的成功 .     

城市交通管理中的出租车规划模型

最佳数量的确定和价格的制定是城市出租车规划中的两个核心问题.对影响出行强度的市区人口和建成区面积分析建立了出行总量预测模型,再通过分析出租车最佳数量与出租车运营里程的关系,得出出租车最佳数量的预测模型.在出租车最佳数量模型的基础上,分析出租车数量的差异和差异产生的原因,给出了出租车定价判断模型,为出租车定价机制的调整指明了方向.最后以长沙市为例进行了实证分析.