Expectation–maximization algorithm

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Related lectures (28)

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Gaussian Mixture Clustering: Expectation-Maximization Algorithms

Explores the Expectation-Maximization algorithm for Gaussian Mixture Clustering and its challenges and practical implementation.

Latent Variable Models

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.

Gaussian Mixture Model: EM final form

Explains the E-step and M-step calculations in the Gaussian Mixture Model, including the pseudocode of the EM algorithm.

Neurobiological Signal Processing

Explores neurobiological signal processing, covering spike modeling, signal classification, and data characterization using principal component analysis.

Gaussian Mixture Regression: Modeling and Prediction

Covers Gaussian Mixture Regression principles, modeling joint and conditional densities for multimodal datasets.

Linear Dimensionality Reduction

Explores linear dimensionality reduction through PCA, variance maximization, and real-world applications like medical data analysis.

Belief Propagation on Graphs

Covers belief propagation on graphs, exploring computation challenges and heuristics, focusing on sparse random graphs' loop properties.

Data Classification: Gaussian Mixture Models

Explores Gaussian Mixture Models for data classification, focusing on denoising signals and estimating original data using likelihood and posteriori approaches.

Data Classification: Mixture Models

Explores EMG signal analysis, mixture models, Gaussian models, and spike sorting in neural signal processing using PCA.

Bayes Estimator, Simulated Annealing and EM

Covers Bayes estimator, Simulated Annealing, and EM for parameter estimation.