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 ...

To recognize stress and emotion, most of the existing methods only observe and analyze speech patterns from present-time features. However, an emotion (especially for stress) can change because it was ...

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 ...

What if you could predict the future, not with a crystal ball, but with math? In this guide, Veritasium explains how a 120-year-old concept called Markov chains has become a silent force shaping ...