Distribution Estimation

Related lectures (30)

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Covers the estimation of distributions using samples and probability models.

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

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

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

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

Covers the concept of property testing using statistical methods.

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

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

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

Explores continuous random variables and their properties, including support and cumulative distribution functions.