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Monte Carlo Algorithms 'Top Down' with Python/Matplotlib
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In general Monte Carlo simulations are considered powerful due to their ability to model the probability of different outcomes in a process based on the intervention of random variables. These strategies can also be applied to random algorithms.
This is the focus of the lecture on 19.10.17 delivered by Ola Aarøen entitled:
"Interactive Illustration of Monte-Carlo Algorithms 'Top Down'" .
Ola is currently a PhD student at the Department of Biotechnology and Food Science, working on coalescence of oil-in-water emulsions. Previous work included path sampling using quantum mechanics/classical force fields on proton transfer in water-trimer systems for prof. Titus van Erp. In this talk, Ola will present methodologies related to Monte-Carlo Algorithms, “Top Down”, through interactive examples and exercises.
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Monte Carlo Algorithms 'Top Down' with Python/Matplotlib
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