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Rothschild Lecture: Hamiltonian Monte Carlo and geometric integration

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GCS - Geometry, compatibility and structure preservation in computational differential equations

Many application
fields require samples from an arbitrary probability distribution. Hamiltonian
Monte Carlo is a sampling algorithm that originated in the physics literature
and has later gained much popularity among statisticians. This is a talk
addressed to a general audience, where I will describe the algorithm and some
of its applications. The exposition requires basic ideas from different fields,
from statistical physics to geometric integration of differential equations and
from Bayesian statistics to Hamiltonian dynamics and I will provide the
necessary background, albeit superficially.

This talk is part of the Isaac Newton Institute Seminar Series series.

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