Kernel density estimation

Related lectures (32)

Explores implicit generative models, covering topics like method of moments, kernel choice, and robustness of estimators.

Delves into SVM hyperparameters, showcasing the impact of C and kernel width on classification results.

Covers conditional density, independence of random variables, expectation, and variance calculation.

Covers property testing against uniform distributions using the plug-in approach.

Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.

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

Covers empirical risk minimization, statistical learning, and examples of cancer prediction, house pricing, and image generation.

Covers the fundamentals of information theory, focusing on source coding and channel coding.

Explores nonparametric relationships in GLMs and the importance of understanding variability through probability and models.

Covers the multivariate normal distribution, properties, and sampling methods.

Explores conditional density, expectations, and independence of random variables with practical examples.

Covers the Markov Chain Monte Carlo method and the Metropolis-Hastings algorithm for generating samples from a target probability distribution.

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