Claude Shannon discovered that language is neither purely random nor fully predictable, and that gap between randomness and ...

Hidden Markov models (HMMs) provide a powerful framework for inferring unobserved processes that evolve over time or space by linking an underlying Markovian state sequence to observed data via ...

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 ...

This paper proposes a hidden state Markov model (HMM) that incorporates workers’ unobserved labor market attachment into the analysis of labor market dynamics. Unlike previous literature, which ...