Efficiency (statistics)

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Related lectures (32)

Page 3 of 4

Monte Carlo: Markov Chains

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

Spiked Matrix Estimation

Covers the AMP algorithm for spiked matrix estimation and its application to low-rank matrix factorization and GLM models.

Estimators and Bias

Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.

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.

Maximum Likelihood: Estimation and Inference

Introduces maximum likelihood estimation, discussing its properties and applications in statistical analysis.

Estimation and Confidence Intervals

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Monte Carlo Estimation: Error Analysis

Covers the Monte Carlo method for generating realizations and unbiased estimators.

Feynman Rules I: Asymptotic Statistic and Instantons

Covers the Feynman Rules, Asymptotic Statistics, Normal Ordering, and Instantons.

Estimation: Measures of Performance

Explores estimation measures of performance, including the Cramér-Rao bound and maximum likelihood estimation.

Kalman Filter: Minimal Variance Estimator

Explores the Kalman filter as a minimal variance estimator and its application in estimating position and velocity.