EM Algorithm for Estimating Unknown Data
- Login to Download
- 1 Credits
Resource Overview
Detailed Documentation
In data science, the EM algorithm, known as the Expectation-Maximization algorithm, is extensively employed across various scenarios. This iterative algorithm is utilized to infer data distributions from incomplete or error-prone datasets. Beyond synchronization applications, it serves clustering, classification, and anomaly detection tasks through its two-phase approach: an E-step computing expected log-likelihood using current parameters, and an M-step updating parameters to maximize this expectation. Variants like Gaussian Mixture Models (implemented via scikit-learn's GaussianMixture class) and Hidden Markov Models (using libraries like hmmlearn) further adapt the algorithm to diverse data types and applications by modeling complex probability distributions.
- Login to Download
- 1 Credits