CORDIS Project
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This project aims to enhance Monte Carlo methods for statistical inference in complex biological models by introducing repulsive distributions. By improving convergence rates and enabling parallel processing, it seeks to make these methods more efficient for applied statisticians and machine learning practitioners.
Expensive computer simulations have become routine in the experimental sciences.
Astrophysicists design complex models of the evolution of galaxies, biologists develop intricate models of cells, ecologists model the dynamics of ecosystems at a world scale. A single evaluation of such complex models takes minutes or hours on today's hardware.
On the other hand, fitting these models to data can require millions of serial evaluations.
Monte Carlo methods, for example, are ubiquitous in statistical…
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
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