Linked from

The 10 pages that link to Hidden Markov model, each with the reason it gives.

• Maximum likelihood estimationRelated: The expectation-maximization algorithm estimates its parameters by maximizing data likelihood.

• Markov chainBroader topic: It extends the chain to situations where states cannot be observed directly.

• Speech recognitionRelated: It was a central framework for representing the progression from hidden speech units to observed audio features.

• BioinformaticsRelated: It can detect genes, motifs, and sequence families despite noisy observations.

• Speech synthesisRelated: It powered many earlier systems that aligned text with speech units.

• Andrey MarkovBroader topic: This widely used model applies Markov’s framework when the states themselves cannot be observed.

• Expectation–maximization algorithmRelated: Its hidden state sequence is a structured latent variable that EM can estimate probabilistically.

• Viterbi algorithmRelated: The algorithm is a standard way to find the most probable hidden-state path in this model.

• Jim SimonsRelated: Methods associated with this model helped inform Renaissance’s early quantitative research.

• Sequence analysis (bioinformatics)Related: Profile models detect distant relatives by representing conserved and variable positions in a sequence family.