An Explanation of the Expectation Maximization Algorithm
2009 (English)Report (Other academic)
The expectation maximization (EM) algorithm computes maximum like-lihood estimates of unknown parameters in probabilistic models involvinglatent variables. More pragmatically speaking, the EM algorithm is an iter-ative method that alternates between computing a conditional expectationand solving a maximization problem, hence the name expectation maxi-mization. We will in this work derive the EM algorithm and show that itprovides a maximum likelihood estimate. The aim of the work is to showhow the EM algorithm can be used in the context of dynamic systems andwe will provide a worked example showing how the EM algorithm can beused to solve a simple system identification problem.
Place, publisher, year, edition, pages
2009. , 19 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2915
Expectation Maximization, system identification, Maximum likelihood, latent variables, probabilistic models.
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-56209ISRN: LiTH-ISY-R-2915OAI: oai:DiVA.org:liu-56209DiVA: diva2:316999