Continuous Relaxations for Discrete Hamiltonian Monte Carlo

The study demonstrates how discrete probabilistic models can be transformed into continuous systems using an extended Gaussian integral trick, providing a principled foundation for applying Hamiltonian Monte Carlo to previously intractable inference tasks.

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