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.
|Hidden Markov model posterior state probabilities|
|Hidden Markov model parameter estimates from emissions and states|
|Hidden Markov model states and emissions|
|Hidden Markov model parameter estimates from emissions|
|Hidden Markov model most probable state path|
Estimate Markov models from data.
Markov chains are mathematical descriptions of Markov models with a discrete set of states.