Experimental Investigation of Distance Graduate Studies of the Open Source Environment by Models of Optimal Sequential Decisions and the Bayesian Approach
Development and applications of the open source software is a new and dynamic field. Important changes often happen in days and weeks. Thus some new non-traditional approaches of education should be investigated to meet the needs of open software adequately.
The general consideration of this problem is difficult. Therefore we start by relevant case studies. In this chapter we consider models of optimal sequential decisions with multiple objective functions as an example. The aim is to show that models can be implemented and updated by graduate students themselves. That reflects the usual procedures of the open source development. This way students not only learn the underlaying model but obtain the experience in the development of open source software.
In this case the step-by-step improvement of the model and software is at least as important as the final result that is never achieved in open source environment as usual. A short presentation of the basic ideas is in [Moc00]. Note that doing this we accumulate some experience in the completely new field of education when all the information can be easily obtained by Internet. The users are doing just the creative part by filtering and transforming the information to meet their own objectives, to build their own models. The natural way is computer experimentation.
To make the task as easy as possible all the algorithms considered in this chapter are implemented as platform independent Java applets or servlets therefore readers can easily verify and apply the results for studies and for real life optimization models.
To address this idea the chapter is arranged in a way convenient for the direct reader participation. Therefore a part of the chapter is written as some ‘user guide’. The rest is a short description of optimization algorithms and models. All the remaining information is on web-sites, for example http : //pilis.if.ktu.lt/~mockus.
Key wordssequential decisions recurrent equations Bayesian approach distance studies
Unable to display preview. Download preview PDF.
- [Bay83]Bayes, T.: An essay towards solving a problem in the doctrine of chances. Phil. Transactions of Royal Society, 53, 370–418 (1783)Google Scholar
- [Bel57]Bellman, R.: Dynamic Programming. Princeton University Press, Princeton, New Jersey (1957)Google Scholar
- [Moc89b]Mockus, J.: Bayesian approach to global optimization and application to constrained and multi-objective problems. In: Abstracts, the Fifth International Conference on Stochastic Programming in Ann Arbor, August 13–18, 1989, (1989)Google Scholar
- [Moc00]Mockus, J.: A Set of Examples of Global and Discrete Optimization: Application of Bayesian Heuristic Approach. Kluwer Academic Publishers (2000)Google Scholar
- [Tij94]Tijms, H.: Stochastic Models. An Algorithmic Approach. Wiley, New York (1994)Google Scholar
- [Zil86]Zilinskas, A.: Global Optimization: Axiomatic of Atatistical Models, Algorithms and Their Applications. Mokslas, Vilnius, Lithuania (1986) in RussianGoogle Scholar