Seonglae ChoExpectation Maximization
Expectation Maximization is an optimization algorithm for finding the maximum likelihood estimate of certain variables. Useful when the parameters are interdependent and cannot be derived analytically.
iterative algorithm that has two main steps
Given there are two interdependent parameter sets A and B
- We fix one set of parameters (A) to calculate the best set of values for the other set (B)
- Use the best value for set B and calculate best values for set A
Iterate until values stabilise
E Step (Expectation step)
calculates the expected complete log likelihood with posterior for fixed parameters
That is why Expectation maximization because It is same to maximize expectation
calculates the expected complete-data log-likelihood for fixed parameters A and Σ
각 데이터 포인트가 어떤 분포에서 생성되었는지에 대한 확률을 추정
M Step (Maximization Step)
re-estimates next step’s fixed parameters to maximizing the expected complete-data log-likelihood (derivative)
확률과 함께 모델 파라미터를 업데이트
