EM- Expectation maximization algorithm and applications

EM- Expectation maximization algorithm and applications

EM algorithm is an iterative method for finding maximum likelihood or Maximum A Posteriri (MAP) extimates of parameters in statistical models where the models depends on unobserved latent variable. EM is used to find maximum likelihood estimates given incomplete samples.

EM algorithm applications

1. Data clustering in machine learning
2. Computer vision
3. Natural Language Processing (NLP)
4. Probabilistic Context free grammars
5. Item response theory models
6. Estimating the parameters of Hidden Markov Model (HMM).