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In the fields of Data Science and Artificial Intelligence, an HMM (Hidden Markov Model) is a statistical model used to predict hidden states based on observable events.
At decision epoch ( t ), solve a schedule using predicted processing time distributions (e.g., expected value or risk‑adjusted). Re‑schedule after each job or after state belief change exceeds threshold.
When viewing an HMM schedule, several specific terms and columns dictate the logistics plan:
Given belief ( b_t ), predict distribution of future processing times: [ P(\texttime for next job) = \sum_s b_t(s) \cdot P(\texttime | s) ]