BIMSA > Seminar on Control Theory and Nonlinear Filtering Maximum Likelihood from Incomplete Data via the EM Algorithm
Maximum Likelihood from Incomplete Data via the EM Algorithm
Organizer
Shing Toung Yau
Speaker
Minli Feng
Time
Wednesday, June 5, 2024 3:00 PM - 3:30 PM
Venue
理科楼A-304
Abstract
We present a general approach to iterative computation of maximum-likelihood estimates when the observations can be viewed as incomplete data. Since each iteration of the algorithm consists of an expectation step followed by a maximization step we call it the EM algorithm. The EM process is remarkable in part because of the simplicity and generality of the associated theory, and in part because of the wide range of examples which fall under its umbrella. When the underlying complete data come from an exponential family whose maximum-likelihood estimates are easily computed, then each maximization step of an EM algorithm is likewise easily computed.