A Bayesian nonparametric model for competing risks yields a prediction curve giving the probability a future event is of a specific type as a function of its occurrence time, plus posterior estimates for survival and incidence functions.
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A hierarchical shrinkage model is introduced for node-parent conditional probabilities in discrete Bayesian networks, enabling posterior sampling and structure learning that handles sparse counts.
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Principled Estimation and Prediction with Competing Risks: a Bayesian Nonparametric Approach
A Bayesian nonparametric model for competing risks yields a prediction curve giving the probability a future event is of a specific type as a function of its occurrence time, plus posterior estimates for survival and incidence functions.
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Learning discrete Bayesian networks with hierarchical Dirichlet shrinkage
A hierarchical shrinkage model is introduced for node-parent conditional probabilities in discrete Bayesian networks, enabling posterior sampling and structure learning that handles sparse counts.