PRIM meta-learns a Model-Averaged Causal Estimation transformer to perform Bayesian RCA by marginalizing structural uncertainty over synthetic causal priors, achieving 17ms inference on systems up to 100 variables.
Parameter priors for directed acyclic graphical models and the characterization of several probability distributions.The Annals of Statistics, 30(5):1412– 1440, October 2002
2 Pith papers cite this work, alongside 248 external citations. Polarity classification is still indexing.
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Reviews asymptotic normality conditions for counting-process REMs under varying limits of n and T, with simulations illustrating effects of modeling choices like windowing and log transforms on Cox-type models.
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PRIM: Meta-Learned Bayesian Root Cause Analysis
PRIM meta-learns a Model-Averaged Causal Estimation transformer to perform Bayesian RCA by marginalizing structural uncertainty over synthetic causal priors, achieving 17ms inference on systems up to 100 variables.
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A Counting Process View of Relational Event Models: Practical Asymptotics
Reviews asymptotic normality conditions for counting-process REMs under varying limits of n and T, with simulations illustrating effects of modeling choices like windowing and log transforms on Cox-type models.