Distribution Estimation

Related lectures (30)

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Covers the estimation of distributions using various methods such as minimum loss and expectation.

Covers the concept of property testing using statistical methods.

Covers the fundamental concepts of statistics and their application in data science.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

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

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

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Covers concentration inequalities and sampling methods for estimating unknown distributions, with a focus on population infection rates.

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