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  "Title": "Search Algorithms and Loss Functions for Bayesian Clustering",
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  "Description": "The SALSO algorithm is an efficient randomized greedy\nsearch method to find a point estimate for a random partition\nbased on a loss function and posterior Monte Carlo samples. The\nalgorithm is implemented for many loss functions, including the\nBinder loss and a generalization of the variation of\ninformation loss, both of which allow for unequal weights on\nthe two types of clustering mistakes. Efficient implementations\nare also provided for Monte Carlo estimation of the posterior\nexpected loss of a given clustering estimate. See Dahl,\nJohnson, Müller (2022) <doi:10.1080/10618600.2022.2069779>.",
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