Non-Parametric Estimation: Kernel Density Estimators

Related lectures (32)

Page 2 of 4

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

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Introduces probability, estimation methods, linear models, testing, and advanced regression techniques.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Explores nonparametric statistics, covering estimation methods and the bias-variance tradeoff.

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Explores optimality in decision theory and unbiased estimation, emphasizing sufficiency, completeness, and lower bounds for risk.

Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.