Poster
in
Workshop: Beyond Bayes: Paths Towards Universal Reasoning Systems
P06: Automatic Inference with Pseudo-Marginal Hamiltonian Monte Carlo
Jinlin Lai
Abstract:
Authors: Jinlin Lai, Daniel Sheldon
Abstract: Pseudo-marginal Hamiltonian Monte Carlo (PM-HMC) is a technique for sampling the parameters from the posterior of Bayesian models. However, its usage within probabilistic programming frameworks is under-explored. We show that PM-HMC can be used to simplify the sampling problem for non-reparameterizable models, which complements existing methods in this area.
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