Work

Continuous Relaxations for Discrete Hamiltonian Monte Carlo

Year: 2012

Type: conference-paper

Abstract: Continuous relaxations play an important role in discrete optimization, but have not seen much use in approximate probabilistic inference. Here we show that a general form of the Gaussian Integral Trick makes it possible to transform a wide class of discrete variable undirected models into fully continuous systems. The continuous representation allows the use of gradient-based Hamiltonian Monte Carlo for inference, results in new ways of estimating normalization constants (partition functions), and in general opens up a number of new avenues for inference in difficult discrete systems. We demonstrate some of these continuous relaxation inference algorithms on a number of illustrative problems.

Source: Edinburgh Research Explorer (University of Edinburgh)

Authors: Yichuan Zhang, Zoubin Ghahramani, Amos Storkey, Charles Sutton

Institutions: University of Edinburgh, University of Cambridge

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: Engineering and Physical Sciences Research Council

Awards: Fast, Locally Adaptive Inference for Machine Learning in Graphical Models, G3400487262, Advanced Bayesian Computation for Cross-Disciplinary Research

OpenAlex ID: W2134949445