Hidden Markov Models Explained: How AI Learns to Predict Sequences #machinelearning

追加: 2026-06-02

[00:00:00]You can't see the weather, but you see whether people carry umbrellas.

[00:00:03]>> [music] >> From observations, infer hidden weather.

[00:00:08]How do you reason about hidden states [music] from visible effects? And why does assuming today depends only on yesterday make this tractable?

[00:00:15]If you knew weather history, predicting observations is easy. But you don't know the weather, you only see umbrella sequences.

[00:00:22]You must infer weather from umbrella observations. This is the hidden Markov model. Hidden states evolve, observations [music] are generated by states.

[00:00:32]Without the Markov assumption, inference is exponential considering all history.

[00:00:36]But assuming today depends only on yesterday makes inference linear.

[00:00:40]This gives rise to elegant linear time inference [music] algorithms like the forward-backward and Viterbi structure.

[00:00:49]In an HMM, we track states [music] and observations. Transitions define state changes and observations link states to what we see.

[00:00:57]Filtering computes current beliefs. The forward algorithm maintains a running belief about today's weather, updating it with each new umbrella [music] observation. HMMs excel in sequence data. In speech, hidden states represent phonemes [music] and observations are acoustic wave features. Running inference on acoustic features, the system finds the most likely phoneme sequence to decode what was spoken.

[00:01:20]This efficiency is thanks to the forward algorithm running in linear time, scaling perfectly with sequence length [music] for real-time analysis.

[00:01:27]Analyzing speech, decoding genes, or tracking objects, HMMs turn noisy observations into hidden truths by [music] disregarding the distant past.