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Identifiability in Finite Mixture Models by Victor Chen is a document available to read on EtoBox.
What is Identifiability in Finite Mixture Models about?
The document discusses the concept of identifiability for finite mixture distributions. It explains that the parameters must be identifiable in order to estimate them from sample data. It then describes the EM algorithm, which is an iterative method used to estimate the parameters of mixture models via maximum likelihood. The EM algorithm treats the component labels as missing data and alternates between an expectation (E) step, where the expected values are computed based on current estimates, and a maximi
- Author
- Victor Chen
- Language
- EN