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The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling
Michael Betancourt
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Oznake: computer science;machine learning
Leveraging the coherent exploration of Hamiltonian flow, Hamiltonian Monte Carlo produces computationally efficient Monte Carlo estimators, even with respect to complex and high-dimensional target distributions. When confronted with data-intensive applications, however, the algorithm may be too expe ...
Leto: 2015 Vir: videolectures.net
Št. zadetkov: 1
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