We propose an interdisciplinary project involving different fields such as Bayesian data analysis, statistical physics and applied mathematics for the efficient exploration and minimization of complex scalar functions. The project is centered on the Nested Sampling optimization algorithm based on Bayesian statistics to explore the relevant parameter space recursively effectively, obtain the topology thereof, and assess the quality and reliability of the obtained optimum within a pre-determined statistical error.

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