Sampling strategies

Related lectures (27)

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Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Explores sampling, inferential statistics, and effective experimentation in statistics.

Covers the estimation of distributions using samples and probability models.

Covers the estimation of extreme quantiles using empirical quantiles and sample data.

Explores sampling in inferential statistics, emphasizing the impact of sample size and randomness on inference accuracy.

Covers data representations and processing techniques essential for effective machine learning algorithms.

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

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

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.