8923918
A Linear Belief Function Approach to Portfolio Evaluation
We show how to use linear belief functions to represent market information and financial knowledge, including complete ignorance, statistical observations, subjective speculations, distributional assumptions, linear relations, and empirical asset pricing models. We then appeal to Dempster's rule of combination to integrate the knowledge for assessing an overall belief on portfolio performance, and to update this belief by incorporating additional information.
14735447
Representing asymmetric decision problems using coarse valuations
A valuation-based system approach to knowledge representation has shown its advantages in improving computational efficiency and in allowing many decision models including belief networks. This study applies the Dempster-Shafer theory of belief functions and extends its framework to allow coarse valuations, which admit incomplete specification of probabilities and utilities and, therefore, are more flexible in representing asymmetric decision problems. It presents an algorithm for making inferences and decisions in systems using coarse valuations. It shows that a coarse valuation-based system provides a most natural and compact representation of decision problems.
5950046
A theory of Gaussian belief functions
Abstract A Gaussian belief function can be intuitively described as a Gaussian distribution over a hyperplane, whose parallel subhyperplanes are the focal elements. This paper elaborates on the idea of Dempster and Shafer and formally represents a Gaussian belief function as a wide-sense inner product and a linear functional over a variable space, and as their duals over a hyperplane in a sample space. By adapting Dempster's rule to the continuous case, it derives a rule of combination and proves its equivalence to its geometric description by Dempster. It illustrates by examples how mixed knowledge involving linear equations, multivariate Gaussian distributions, and partial ignorance can be represented and combined as Gaussian belief functions.
38352937
Z User Studio: An integrated support tool for Z specifications
This paper introduces an integrated Z support tool, Z User Studio (ZUS), which was developed by the Formal Methods Research Group of Shanghai University. ZUS is an interactive, single-user tool, running under MS-Windows and NT It supports the production of well-formed Z specifications by providing facilities for building, editing, checking and viewing Z specification documents. It displays the Z constructs and symbols, as they would appear in most popular book on Z ZUS has two modes for editing specifications, text-based editor and graphical editor. The graphical editor provides a hierarchical structure directory for every current Z specification. ZUS implements a computer aided test cases generator (TCG).
12389317
Methodical restructuring of complex workflow activities
We describe a family of activity-split and activity-join operations with a notion of validity. The key idea of introducing the set of activity-split and activity-join operations is to allow users to restructure ongoing activities in anticipation of uncertainty so that any significant performance loss due to unexpected unavailablity or delay of shared resources can be avoided or reduced through release of early committed resources or transferring ownership of uncommitted resources. To guarantee the correctness of new activities generated by activity-split or activity-join operations, we define the notion of validity of activity restructuring operations and identify the cases where the correctness is ensured and the cases where activity-split or activity-join are illegal due to the inconsistency incurred.
599285
Differential evaluation of continual queries
We define continual queries as a useful tool for monitoring of updated information. Continual queries are standing queries that monitor the source data and notify the users whenever new data matches the query. In addition to periodic refresh, continual queries include Epsilon Transaction concepts to allow users to specify query refresh based on the magnitude of updates. To support efficient processing of continual queries, we propose a differential re-evaluation algorithm (DRA), which exploits the structure and information contained in both the query expressions and the database update operations. The DRA design can be seen as a synthesis of previous research on differential files, incremental view maintenance, and active databases.
34284992
An Algorithmic Approach for Sensitivity Analysis of Perturbed Quasi-Birth-and-Death Processes
In this paper, we present an algorithmic approach for sensitivity analysis of stationary and transient performance measures of a perturbed continuous-time level-dependent quasi-birth-and-death (QBD) process with infinitely-many levels. By developing a new LU-type RG-factorization using the censoring technique, we obtain the maximal negative inverse of the infinitesimal generator of the QBD process. The derivatives of the stationary performance measures of the QBD process can then be expressed and computed in terms of the maximal negative inverse, overcoming the computational difficulty arising from the use of group inverses of infinite size in the current literature (see Cao and Chen [11]). We also use a stochastic integral functional to study the transient performance measure of the QBD process and show how to use the algorithmic approach for its sensitivity analysis. As an example, a perturbed MAP/PH/1 queue is also analyzed.
17945106
An object-oriented approach to multi-level association rule mining
Previous studies in data mining have yielded efficient algorithms for discovering association rules. To increase the expressiveness and relevance of the discovered rules, researche~s typically augment the thresholds with domain-specific knowledge. Most existing proposals use the relational approach to organize and maintain these multi-level concept hierarchies. We argue that an object-oriented approach is better suited for focusing the search and regulating the mining of association rules both at multi-levels within one concept hierarchy, and across multiple concept hierarchies. We propose an adaptive encodhtg scheme for focusing the mining on semantically deeper and more informative knowledge. We demonstrate that the application of an object-oriented approach provides us with the benefits of a flexible combination of multiple multi-level concept hierarchies for focusing the mining on more informative and refined knowledge, and also allows us to enjoy seamless integration of multi-level concept hierarchies with legacy databases. In addition, by using an adaptive encoding scheme, efficient algorithms developed for discovering association rules can be integrated into the object-oriented framework with no or little extm cost.