A Note on Bayesian Estimation for the Negative-Binomial Model

2000 Mathematics Subject Classification: 62F15.

Data Flow Analysis and the Linear Programming Model

* The research is supported partly by INTAS: 04-77-7173 project, http://www.intas.be

Learning Technology in Scheduling Based on the Mixed Graphs

We propose the adaptive algorithm for solving a set of similar scheduling problems using learning technology. It is devised to combine the merits of an exact algorithm based on the mixed graph model and heuristics oriented on the real-world scheduling problems. The former may ensure high quality of the solution by means of an implicit exhausting enumeration of the feasible schedules. The latter may be developed for certain type of problems using their peculiarities. The main idea of the learning technology is to produce effective (in performance measure) and efficient (in computational time) heuristics by adapting local decisions for the scheduling problems under consideration. Adaptation is realized at the stage of learning while solving a set of sample scheduling problems using a branch-and-bound algorithm and structuring knowledge using pattern recognition apparatus.

Algorithms for Evaluation of the Wright Function for the Real Arguments’ Values

2000 Math. Subject Classification: 33E12, 65D20, 33F05, 30E15

On Y. Nievergelt's Inversion Formula for the Radon Transform

Mathematics Subject Classification 2010: 42C40, 44A12.

The Notion of Model in Software Engineering

Милослав A. Средков - Понятията модел и моделиране се използват толкова интензивно в много дисциплини, че е трудно да им се придаде конретно значение. Дори в Софтуерните технологии, разбирането на тези понятия силно зависи от контекста. Ние смятаме, че това допринася за неконсистентността между подходите за моделиране там. В тази статия посочваме някои от произтичащите проблеми, както и важността да има подходяща дефиниция, съчетана с подходящи инструменти. Преглеждаме по-общите дефиниции на модел отвъд границите на Софтуерните технологии. Минаваме през моделирането на различни вторични продукти на процесите от Софтуерните технологии. Накрая представяме нашата визия относно използването на такава обща основа за наша полза.

Bayesian and Frequentist Two-Sample Predictions of the Inverse Weibull Model Based on Generalized Order Statistics

2000 Mathematics Subject Classification: 62E16,62F15, 62H12, 62M20.

Theorems on the Convergence of Series in Generalized Lommel-Wright Functions

Mathematics Subject Classification: 30B10, 30B30; 33C10, 33C20