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Probability distributions from Riemannian geometry, generalized hybrid Monte Carlo sampling, and path integrals

2011
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Three-Dimensional Imaging, Interaction, and Measurement
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When considering probabilistic pattern recognition methods, especially methods based on Bayesian analysis, the probabilistic distribution is of the utmost importance. However, despite the fact that the geometry associated with the probability distribution constitutes essential background information, it is often not ascertained. This paper discusses how the standard Euclidian geometry should be generalized to the Riemannian geometry when a curvature is observed in the distribution. To this end,

doi:10.1117/12.872862
dblp:conf/3dica/PaquetV11
fatcat:qjjhcnw53raivltufdf5kpzyxe