Speaker
Frederic Beaujean
(MPI Munich)
Description
Adaptive importance sampling, or population Monte Carlo (PMC), is a
powerful technique to sample from and integrate over complicated
distributions that may include degeneracies and multiple modes in up
to roughly 40 dimensions. PMC is best for tough problems as the
costly evaluation of the target distribution can be massively
parallelized.
Based on a simplified global fit for new physics, the individual parts
of the algorithm ranging from the initialization over
proposal-function updates to the final results are presented step by
step in a hands-on and visual fashion. Only basic knowledge of C++ is
required in order to modify the given source-code examples for a more
rewarding learning experience.
