Documentation

This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

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.

Functions

hmmdecodeHidden Markov model posterior state probabilities
hmmestimateHidden Markov model parameter estimates from emissions and states
hmmgenerateHidden Markov model states and emissions
hmmtrainHidden Markov model parameter estimates from emissions
hmmviterbiHidden Markov model most probable state path

Examples and How To

Hidden Markov Models (HMM)

Estimate Markov models from data.

Concepts

Markov Chains

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

Was this topic helpful?