AI RESEARCH
Heterogeneous Variational Inference for Markov Degradation Hazard Models: Discretized Mixture with Interpretable Clusters
arXiv CS.LG
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ArXi:2604.24818v1 Announce Type: new Bayesian finite mixture models can identify discrete risk clusters (low-risk vs. high-risk equipment), but face three critical bottlenecks: (1) insufficient degradation signals from coarse state discretization, (2) unstable cluster identification when data inherently s fewer clusters than explored, and (3) computational infeasibility of Marko Chain Monte Carlo (MCMC) methods for production deployment (7+ hours per model