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Hidden Markov Models

Markov models for data generation

Markov processes are examples of stochastic processes—processes that generate random sequences of outcomes or states according to certain probabilities. Markov processes are distinguished by being memoryless—their next state depends only on their current state, not on the history that led them there. Models of Markov processes are used in a wide variety of applications, from daily stock prices to the positions of genes in a chromosome. Hidden Markov Models (HMM) seek to recover the sequence of states that generated a given set of observed data.


hmmdecode Hidden Markov model posterior state probabilities
hmmestimate Hidden Markov model parameter estimates from emissions and states
hmmgenerate Hidden Markov model states and emissions
hmmtrain Hidden Markov model parameter estimates from emissions
hmmviterbi Hidden Markov model most probable state path

Examples and How To

Hidden Markov Models (HMM)

Estimate Markov models from data.


Markov Chains

Markov chains are mathematical descriptions of Markov models with a discrete set of states.

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