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Multivariate kernel density estimation
Formal sciences
Statistics
Statistical inference
Non-parametric statistics
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Related lectures (31)
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Parametric Models
Explores statistical estimation, regression models, and model selection in parametric models.
Maximum Likelihood Estimation: Density Estimation and Bernoulli Model
Explores maximum likelihood estimation for density and Bernoulli model, including test reliability and disease screening.
Topic Models
Introduces topic models, covering clustering, GMM, LDA, Dirichlet distribution, and variational inference.
Mathematics of Data: Models and Learning
Explores models, learning paradigms, and applications in Mathematics of Data.
Basic Principles of Point Estimation
Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.
Elliptical Distributions: Properties and Applications
Covers elliptical distributions, including properties, applications, and risk management implications.
Implicit Generative Models
Explores implicit generative models, covering topics like method of moments, kernel choice, and robustness of estimators.
Topic Models: Latent Dirichlet Allocation
Covers topic models, focusing on Latent Dirichlet Allocation, clustering, GMMs, Dirichlet distribution, LDA learning, and applications in digital humanities.
Quantiles, Sampling, Histogram Density
Explores quantiles, sampling, and histogram density for understanding distributions and constructing confidence intervals.
Introduction to Machine Learning: Basics and Examples
Introduces the basics of machine learning, covering supervised learning, reinforcement learning, and dimension reduction.
Nonparametric GLMs & High Level Picture
Explores nonparametric relationships in GLMs and the importance of understanding variability through probability and models.
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