Introduces TSBM, a new Bayesian model for directed networks that enforces ordered blocks via transitivity-inducing priors on directional imbalance and jointly infers block count with an age-ordered partition prior.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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stat.ME 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
A deterministic compression method reduces high-dimensional discrete data to low-dimensional continuous representations that are injective, approximately Gaussian, and preserve cluster centroid separation for efficient model-based clustering.
POTTERS extends the Potts model with generalized spatial dependence and external priors for Bayesian remote sensing image segmentation via variational inference, without needing target-region labels.
citing papers explorer
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Ordering Stochastic Block Models via prior transitivity
Introduces TSBM, a new Bayesian model for directed networks that enforces ordered blocks via transitivity-inducing priors on directional imbalance and jointly infers block count with an age-ordered partition prior.
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Data compression for fast dimension reduction and clustering of high-dimensional discrete data
A deterministic compression method reduces high-dimensional discrete data to low-dimensional continuous representations that are injective, approximately Gaussian, and preserve cluster centroid separation for efficient model-based clustering.
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Scalable Bayesian Spatial Mixture Modelling for Remote Sensing Image Segmentation
POTTERS extends the Potts model with generalized spatial dependence and external priors for Bayesian remote sensing image segmentation via variational inference, without needing target-region labels.