Difference between a mixture model and HHM If we examine a single time slice of the model, it can be seen as a mixture distribution with component densities given by It can be interpreted as an extension of a mixture model where the choice of mixture component for each observation is not independent but depends… Continue reading Hidden Markov model
Markov Models A way to exploit special sequential aspect (e.g. correlations between observations that are close in the sequence). For example, rainy day or not. If we treat the data as i.i.d., then the only information we glean from the data is the frequency of rainy days without any weather trends that last few days.… Continue reading What is a Markov model?
What is sequential data? Data with poor or no i.i.d assumption Often found in time series data. For instance, Rainfall measurements on successive days at a particular location Daily values of a currency exchange rate Acoustic features at successive time frames used for speech recognition) Stationary vs. nonstationary sequential distributions Stationary: data evolves in time… Continue reading Sequential data