salso - Search Algorithms and Loss Functions for Bayesian Clustering
The SALSO algorithm is an efficient randomized greedy search method to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. The algorithm is implemented for many loss functions, including the Binder loss and a generalization of the variation of information loss, both of which allow for unequal weights on the two types of clustering mistakes. Efficient implementations are also provided for Monte Carlo estimation of the posterior expected loss of a given clustering estimate. See Dahl, Johnson, Müller (2022) <doi:10.1080/10618600.2022.2069779>.
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7.18 score 5 stars 6 dependents 153 scripts 886 downloadsfangs - Feature Allocation Neighborhood Greedy Search Algorithm
A neighborhood-based, greedy search algorithm is performed to estimate a feature allocation by minimizing the expected loss based on posterior samples from the feature allocation distribution. The method is described in Dahl, Johnson, and Andros (2023) "Comparison and Bayesian Estimation of Feature Allocations" <doi:10.1080/10618600.2023.2204136>.
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3.95 score 3 stars 1 scripts 413 downloadsgourd - Shrinkage Partition Distribution
Implementation of the distribution introduced in our paper "Dependent Random Partitions by Shrinking Toward an Anchor".
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rustcargo
2.00 score 6 scripts