Bayesian model selection, Posterior distribution, Inference engine

The brute force method is a typical approach for solving unknown or non-standard problems: Without having a more appropriate tool or method, one tries to examine all relevant (“imaginable ”) candidates whether they satisfy the problem statement or not. As demonstrated below, it works effectively with many otherwise hardly solvable problems. Despite its undisputable value, it is rarely taught at universities. Time consumption might be the cause - it is its main disadvantage in general. To speed up the process, one should look for a clue or a hypothesis leading to its higher effectiveness. Using three examples which are otherwise unsolvable since the students lack the resources to do so, e.g. other more sophisticated methods which are beyond their current curricula, we explain its two principal steps: The generation of space of relevant candidates and their testing. Depending on the space size and complexity of their evaluation, the testing phase may last long. Then, computational power becomes a value. In our below examples, spreadsheet calculations are effectively exploited. In this way, the students may start understanding how to combine mathematics and IT in solving complex application problems - one of the aims of STEAM education. In Conclusions, we point to the fact that there are several ways to demonstrate interrelations between traditional Maths and IT. © M.M Cover and graphics design by Mirek Majewski, photo Andrew Shiva/Wikipedia.