An intrinsic effective sample size for manifold MCMC is defined via kernel discrepancy as the number of independent draws yielding equivalent expected squared discrepancy to the target.
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Machine learning clustering of meteor observations produces a new hardness classification H_class that refines traditional Kb models using more parameters and reveals compositional structure in meteoroid populations.
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Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy
An intrinsic effective sample size for manifold MCMC is defined via kernel discrepancy as the number of independent draws yielding equivalent expected squared discrepancy to the target.
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A Machine Learning Approach to Meteor Classification
Machine learning clustering of meteor observations produces a new hardness classification H_class that refines traditional Kb models using more parameters and reveals compositional structure in meteoroid populations.