Hamiltonian Annealed Importance Sampling for partition function estimation

Benjamin J. Culpepper; Jascha Sohl-Dickstein

Hamiltonian Annealed Importance Sampling for partition function estimation

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arxiv 1205.1925 v1 pith:RE54BUSZ submitted 2012-05-09 cs.LG physics.data-an

Hamiltonian Annealed Importance Sampling for partition function estimation

Jascha Sohl-Dickstein , Benjamin J. Culpepper This is my paper

classification cs.LG physics.data-an

keywords modelsannealedcompareexpertsgenerativehamiltonianimportancelaplace

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We introduce an extension to annealed importance sampling that uses Hamiltonian dynamics to rapidly estimate normalization constants. We demonstrate this method by computing log likelihoods in directed and undirected probabilistic image models. We compare the performance of linear generative models with both Gaussian and Laplace priors, product of experts models with Laplace and Student's t experts, the mc-RBM, and a bilinear generative model. We provide code to compare additional models.

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Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond
stat.ML 2025-02 unverdicted novelty 7.0

Derives Õ(d β² A² / ε⁴) oracle complexity for AIS estimating normalizing constant Z to relative error ε and introduces reverse diffusion sampler for geometric paths with large action.