A discretized finite mixture model with ADVI identifies interpretable low- and high-risk clusters in Markov degradation hazard models for 280 industrial pumps, achieving 84x speedup over NUTS while enforcing stability constraints.
The No-U- Turn Sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo,
2 Pith papers cite this work. Polarity classification is still indexing.
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Bayesian random effects estimation followed by stratified DirectLiNGAM causal discovery on 112 pumps shows 400x larger causal effects from operational features in slower-deteriorating equipment compared to faster-deteriorating ones.
citing papers explorer
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Heterogeneous Variational Inference for Markov Degradation Hazard Models: Discretized Mixture with Interpretable Clusters
A discretized finite mixture model with ADVI identifies interpretable low- and high-risk clusters in Markov degradation hazard models for 280 industrial pumps, achieving 84x speedup over NUTS while enforcing stability constraints.
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Understanding Deterioration Random Effects for Causal Discovery in Infrastructure Management
Bayesian random effects estimation followed by stratified DirectLiNGAM causal discovery on 112 pumps shows 400x larger causal effects from operational features in slower-deteriorating equipment compared to faster-deteriorating ones.