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A General Metric for Riemannian Manifold Hamiltonian Monte Carlo
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A General Metric for Riemannian Manifold Hamiltonian Monte Carlo
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Markov Chain Monte Carlo (MCMC) is an invaluable means of inference with complicated models, and Hamiltonian Monte Carlo, in particular Riemannian Manifold Hamiltonian Monte Carlo (RMHMC), has demonstrated impressive success in many challenging problems. Current RMHMC implementations, however, rely on a Riemannian metric that limits their application to analytically-convenient models. In this paper I propose a new metric for RMHMC without these limitations and verify its success on a distribution that emulates many hierarchical and latent models.
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Cited by 1 Pith paper
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A Flat Connection: The Pooling Factor and the Geometry of Centring in Hierarchical MCMC
The Ehresmann connection induced by the Fisher metric on the hierarchical parameter fiber bundle is flat for any smooth posterior, identifying the mixing obstruction as the prior fraction (pooling factor) rather than ...
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