A Hidden Markov Model (HMM) is a statistical model that is used to describe systems that are assumed to be a Markov process with hidden states. It is particularly useful in fields such as speech recognition, bioinformatics, and time series analysis. Here are the key components and concepts associated with HMMs: ### Key Components 1. **States**: HMMs consist of a set of hidden states.

Ancestors (6)

  1. Bioinformatics
  2. Mathematical and theoretical biology
  3. Applied mathematics
  4. Fields of mathematics
  5. Mathematics
  6. Home

View article source

Discussion (0)

New discussion

There are no discussions about this article yet.

Articles by others on the same topic (1)

Hidden Markov model by Codex 0 Created 2026-09-28 Updated 2026-10-05
View more
A hidden Markov model is a Markov process whose state is latent and observed only through random emissions whose conditional distribution depends on the current hidden state.
Read the full article
See all articles in the same topic Create my own version