Resampling (statistics)

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Related lectures (32)

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Approximate Query Processing: BlinkDB

Introduces BlinkDB, a framework for approximate query processing using sampling techniques.

Nonparametric Statistics: Estimation and Optimization

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

Variance Reduction Techniques

Covers variance reduction techniques in stochastic simulation to improve accuracy and efficiency.

Describing Data: Statistics & Uncertainty

Introduces descriptive statistics, uncertainty quantification, and variable relationships, emphasizing the importance of statistical interpretation and critical analysis.

Stochastic Blockmodel Estimation

Explores Stochastic Blockmodel estimation, spectral clustering, network modularity, Laplacian matrix, and k-means clustering.

Model Assessment: Metrics and Selection

Explores model assessment metrics, selection techniques, bias-variance tradeoff, and handling skewed data distributions in machine learning.

Prediction tests

Explores out-of-sample validation and the methodology of cross-validation for testing predictive models.

Cross-validation & Regularization

Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

Efficient Computation of Heat Capacity

Covers the efficient computation of heat capacity in metal organic frameworks using quantum classical methods and explores advanced PIMD techniques beyond benchmarks.

Monte-Carlo Methods for Reinforcement Learning

Explores Monte-Carlo methods for reinforcement learning, comparing them with TD-methods and emphasizing the efficiency of TD methods in propagating information.