The authors replace discontinuous precedence and frontier constraints in a partial-order model with smooth surrogates, producing a continuous posterior that supports gradient MCMC and variational inference while recovering the hard model in the limit.
Structure discovery in bayesian networks by sampling partial orders.Journal of Machine Learning Research, 17(57):1–47, 2016
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A Differentiable Bayesian Relaxation for Latent Partial-Order Inference
The authors replace discontinuous precedence and frontier constraints in a partial-order model with smooth surrogates, producing a continuous posterior that supports gradient MCMC and variational inference while recovering the hard model in the limit.