Abstract
The latent structure inherent in mixture models offers a fitting scenario for the renowned EM algorithm. In Chap. 7, we begin by offering a general overview and then get into comprehensive explanations of the EM algorithm’s application in computing the maximum likelihood estimate for finite mixture models. This chapter also addresses the critical matter of algorithm convergence, taking into consideration the global convergence theorem. Additionally, it provides specific insights into the workings of the EM algorithm.
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Chen, J. (2023). Finite Mixture MLE and EM Algorithm. In: Statistical Inference Under Mixture Models. ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-99-6141-2_7
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DOI: https://doi.org/10.1007/978-981-99-6141-2_7
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