关于序列覆盖紧映射

本文利用了ｃｓ网、序列邻域网、序列开网和弱基的概念,讨论了空间中点正则覆盖,一致覆盖和点有限覆盖的点星网之间的关系．建立了度量空间在几类序列覆盖（紧）映射下象空间的特征,特别地证明了度量空间的序列覆盖（或１序列覆盖）紧映象等价于具有点正则ｃｓ网的空问,回答了Ｔａｎａｋａ等提出的一个问题．     

度量空间的序列覆盖边界紧映象(英文)

本文主要讨论了度量空间的序列覆盖边界紧映象.用序列商、序列覆盖或1-序列覆盖的纤维边界紧或有限来刻画具有sn网或弱基的空间.主要结果如下:(1)度量空间上的序列覆盖边界紧映射是1-序列覆盖映射;(2)空间X是度量空间的序列商边界紧映象当且仅当X是snf-第一可数空间;(3)空间X是度量空间的序列覆盖边界紧S映象当且仅当X有点可数sn-网.     

有限重收敛序列

本文给出序列式序大于n的序列式空间的一个典型例子.作为其应用,我们给出一些序列空间乘积空间的序列式序.     

星可数k网与序列覆盖s映射

证明了在空间具有星可数ｋ网的条件下,度量空间的１（２）序列覆盖ｓ映象是局部可分度量空间的１（２）序列覆盖、紧覆盖ｓ映象。     

局部可分度量空间的序列覆盖s象

本文给出了局部可分度量空间的1序列覆盖s象,2序列覆盖s象,强序列覆盖s象,序列覆盖s象及子序列覆盖s象的内在刻划．从而使关于对度量空间的各类s象的内在刻划方面的研究更趋于完整．     

星可数κ网与序列覆盖S映射

证明了在空间具有星可数κ网的条件下,度量空间的1(2)序列覆盖s映象是局部可分度量空间的1(2)序列覆盖、紧覆盖s映象.     

关于序列覆盖s映射的注记

本文分别给出局部可分度量空间的强序列覆盖（1序列覆盖,2序列覆盖）。映象的新刻画,还分别给出拓扑空间是局部可分度量空间的序列覆盖（紧覆盖）s映象的一个充分条件．     

度量空间的序列商,k-映象

本文给出了度量空间序列商．肛映象的-些内部刻画。证明了空间X是度量空间的序列商。肛映象当且仅当X具有紧有限k-闭cs*-覆盖列的点星sn-网，当且仅当X具有紧有限k-闭覆盖列的点星网．作为上述结果的-个推论．不仅得到了空间X是度量空间序列商，k-映象当且仅当X是度量空间的k-映象，而且还证明了空间X是度量空间当且仅当X具有局部有限(紧有限)闭(肛闭)覆盖列的点星弱邻域网．这里“闭”(“k闭”)不能省略．     

σ -局部有限集族与度量空间的σ -映象

利用σ-映射建立了具有σ-局部有限cs-网、σ-局部有限cs*-网、σ-局部有限序列邻域网、σ-局部有限序列开网的空间与度量空间确定的σ-映象之间的联系.     

前馈序列与反馈序列的关系

本文引入移位寄存器序列的向量值表示讨论了线性移位寄存器的前馈序列与反馈序列同时为ｍ序列时的关系,得到了它们在向量值表示下的一个关系式。     

基于显示、生成偏好的序贯模糊选择函数的研究

本文分别在普通情况以及模糊情况下,对显示偏好、生成偏好合理化的序贯选择函数进行了一定的研究。首先,研究了该序贯合理的选择函数的选择结果与选择顺序无关的条件,其次,研究了选择函数与该序贯合理的选择函数之间的关系。     

序列网与度量空间的序列商映象

In this paper the concept of sequential networks isintroduced, the relations among that and the concepts of sequentialquasi-bases and cs^*-networks are established, and some newcharacterizations of the certain quotient images of metric spaces areobtained.     

关于序贯检验的Bayes分析

本文将充分利用验前信息的Ｂａｙｅｓ方法与序贯检验相结合,推导出了Ｂａｙｅｓ序贯检验方法。文章从表面形式上的决策方案推导到实质上真正的决策方案;并对飞行器落点密集度进行了模拟计算的具体分析,最后得出结论,Ｂａｙｅｓ序贯检验方法对具有昂贵成本的产品试验和我国航天试验有较大的应用价值．     

Sequential Lagrangian Conditions for Convex Programs with Applications to Semidefinite Programming

In this paper it is shown that, in the absence of any regularity condition, sequential Lagrangian optimality conditions as well as a sequential duality results hold for abstract convex programs. The significance of the results is that they yield the standard optimality and duality results for convex programs under a simple closed-cone condition that is much weaker than the well-known constraint qualifications. As an application, a sequential Lagrangian duality, saddle-point conditions, and stability results are derived for convex semidefinite programs.The authors are grateful to the referee and Professor Franco Giannessi for valuable comments and constructive suggestions which have contributed to the final preparation of the paper.     

A sequential pattern mining algorithm using rough set theory

Sequential pattern mining is a crucial but challenging task in many applications, e.g., analyzing the behaviors of data in transactions and discovering frequent patterns in time series data. This task becomes difficult when valuable patterns are locally or implicitly involved in noisy data. In this paper, we propose a method for mining such local patterns from sequences. Using rough set theory, we describe an algorithm for generating decision rules that take into account local patterns for arriving at a particular decision. To apply sequential data to rough set theory, the size of local patterns is specified, allowing a set of sequences to be transformed into a sequential information system. We use the discernibility of decision classes to establish evaluation criteria for the decision rules in the sequential information system.     

Sequential Monte Carlo Methods for Option Pricing

In this article, we provide a review and development of sequential Monte Carlo (SMC) methods for option pricing. SMC are a class of Monte Carlo-based algorithms, that are designed to approximate expectations w.r.t a sequence of related probability measures. These approaches have been used successfully for a wide class of applications in engineering, statistics, physics, and operations research. SMC methods are highly suited to many option pricing problems and sensitivity/Greek calculations due to the nature of the sequential simulation. However, it is seldom the case that such ideas are explicitly used in the option pricing literature. This article provides an up-to-date review of SMC methods, which are appropriate for option pricing. In addition, it is illustrated how a number of existing approaches for option pricing can be enhanced via SMC. Specifically, when pricing the arithmetic Asian option w.r.t a complex stochastic volatility model, it is shown that SMC methods provide additional strategies to improve estimation.     

Monte Carlo Methods for Bayesian Analysis of Survival Data Using Mixtures of Dirichlet Process Priors

Consider the model in which the data consist of possibly censored lifetimes, and one puts a mixture of Dirichlet process priors on the common survival distribution. The exact computation of the posterior distribution of the survival function is in general impossible to obtain. This article develops and compares the performance of several simulation techniques, based on Markov chain Monte Carlo and sequential importance sampling, for approximating this posterior distribution. One scheme, whose derivation is based on sequential importance sampling, gives an exactly iid sample from the posterior for the case of right censored data. A second contribution of this article is a battery of programs that implement the various schemes discussed here. The programs and methods are illustrated on a dataset of interval-censored times arising from two treatments for breast cancer.     

Continuous extension of maps between sequential cascades

The contour of a family of filters along a filter is a set-theoretic lower limit. Topologicity and regularity of convergences can be characterized with the aid of the contour operation. Contour inversion is studied, in particular, for iterated contours of sequential cascades. A related problem of continuous extension of maps between maximal elements of sequential cascades to full subcascades is solved in full generality.     

Sequential optimality conditions for composed convex optimization problems

Using a general approach which provides sequential optimality conditions for a general convex optimization problem, we derive necessary and sufficient optimality conditions for composed convex optimization problems. Further, we give sequential characterizations for a subgradient of the precomposition of a K-increasing lower semicontinuous convex function with a K-convex and K-epi-closed (continuous) function, where K is a nonempty convex cone. We prove that several results from the literature dealing with sequential characterizations of subgradients are obtained as particular cases of our results. We also improve the above mentioned statements.