Density estimation

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Related lectures (32)

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Information Measures

Covers information measures like entropy and Kullback-Leibler divergence.

Spin Glasses and Bayesian Estimation

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

Non-Parametric Estimation: Kernel Density Estimators

Explores non-parametric estimation using kernel density estimators to estimate distribution functions and parameters, emphasizing bandwidth selection for optimal accuracy.

Parameter Estimation of SDEs with Linear Response Theory

Explores parameter estimation of SDEs using Linear Response Theory and covers challenges, examples, algorithms, and convergence.

Nonparametric Statistics: Bayesian Approach

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

Measure Concentration I: Laplace Transform Method

Explores Laplace transform method for measuring concentration and estimating bounds.

Probability and Statistics II: Estimation and Hypothesis Testing

Covers the Central Limit Theorem, confidence intervals, hypothesis testing, and qualities of estimators.

Continuous Random Variables

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Probabilistic Models for Linear Regression

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

Kernel Density Estimation: Bandwidth Selection and Curse of Dimensionality

Covers Kernel Density Estimation focusing on bandwidth selection, curse of dimensionality, bias-variance tradeoff, and parametric vs nonparametric models.