Metropolis-Hastings with Levy jumps prevents entrapment in weighted random walks, yielding a convergence rate that accounts for data heterogeneity, network spectral gap, and jump probability.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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A new joint Bayesian framework allows accurate estimation of both generator and network parameters in DAE power system models, demonstrated on IEEE 9-bus and 39-bus test cases.
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Decentralized Learning via Random Walk with Jumps
Metropolis-Hastings with Levy jumps prevents entrapment in weighted random walks, yielding a convergence rate that accounts for data heterogeneity, network spectral gap, and jump probability.
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DAE-Aware Bayesian Inference for Joint Generator-Network Parameter Estimation
A new joint Bayesian framework allows accurate estimation of both generator and network parameters in DAE power system models, demonstrated on IEEE 9-bus and 39-bus test cases.