
This paper proposes a novel, model-agnostic method for summarizing posterior inference in Bayesian nonparametric mixture models, prioritizing density estimation of the mixing measure using sliced optimal transport distances.


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a-novel-approach-for-summarizing-posterior-inference-in-bayesian-nonparametric-mixture-models-using-sliced-optimal-transport-metrics


A Novel Approach for Summarizing Posterior Inference in Bayesian Nonparametric Mixture Models Using Sliced Optimal Transport Metrics
