The objective is to investigate two emerging information technologies in graduate studies and scientific cooperation. Internet is the first technology. The open source is the second. They help each other in many ways. We investigate the joint influence of both.

Results of complexity theory show the limitations of exact analysis. That explains popularity of heuristic algorithms. It is well known that efficiency of heuristics depends on the parameters. Thus we need some automatic procedures for tuning the heuristics. That helps comparing results of different heuristics. This enhance their efficiency, too.

An initial presentation of the basic ideas is in (Mockus, 2000). Preliminary results of distance graduate studies are in (Mockus, 2006a). Examples of optimization of sequential statistical decisions are in (Mockus, 2006b).

In this paper the theory and applications of Bayesian Heuristic Approach are discussed. In the next paper examples of Bayesian Approach to automated tuning of heuristics will be regarded. The examples of traditional methods of optimization including applications of linear and dynamic programming will be investigated in the last paper. These papers represents three parts of the same work. However each part can be read independently.

All the algorithms are implemented as platform independent Java applets or servlets. Readers can easily verify and apply the results for studies and for real life optimization models.

The information is on the main web-site http://pilis.if.ktu.lt/âˆ¼jmockus and four mirrors.