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Experimental Investigation of Distance Graduate Studies of the Open Source Environment by Models of Optimal Sequential Decisions and the Bayesian Approach

In: Models and Algorithms for Global Optimization

Author

Listed:
  • Jonas Mockus

    (Institute of Mathematics and Informatics
    Kaunas Technological University)

Abstract

Summary 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.

Suggested Citation

  • Jonas Mockus, 2007. "Experimental Investigation of Distance Graduate Studies of the Open Source Environment by Models of Optimal Sequential Decisions and the Bayesian Approach," Springer Optimization and Its Applications, in: Aimo Törn & Julius Žilinskas (ed.), Models and Algorithms for Global Optimization, pages 249-274, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-36721-7_16
    DOI: 10.1007/978-0-387-36721-7_16
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    Cited by:

    1. Irina Vinogradova-Zinkevič, 2021. "Application of Bayesian Approach to Reduce the Uncertainty in Expert Judgments by Using a Posteriori Mean Function," Mathematics, MDPI, vol. 9(19), pages 1-23, October.

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