Sparse early-stage data limits accurate geological risk assessment, increasing the chance of undetected hazards ahead of the TBM. By integrating borehole-derived information through an observation ...
Hidden Markov models (HMMs) provide a robust statistical framework for analysing sequential data by assuming that the observed processes are driven by underlying, unobserved states. These models have ...
A new process—the factorial hidden Markov volatility (FHMV) model—is proposed to model financial returns or realized variances. Its dynamics are driven by a latent volatility process specified as a ...
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