Hidden Markov Model

Definitions

1. A probabilistic sequence model with hidden states that emit observable outputs over time. It influences how models are trained, evaluated, or served, and it can materially change accuracy, robustness, latency, cost, or interpretability. Practitioners usually track it alongside data quality, compute limits, and validation results when moving models into production.

   In plain English: Hidden Markov Model is an AI concept that affects how a model learns, predicts, or gets deployed. It matters because it changes quality, speed, or reliability.

   Example: "We revisited Hidden Markov Model during model evaluation because the first run looked fine offline but behaved poorly in production, and the adjustment improved quality without breaking our latency or compute budget."

Related Terms

• RLHF
• Latent Space
• CPT
• Flow Matching
• Gated Recurrent Unit
• Likelihood
• Logprob
• Token Probability