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Related lectures (32)
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Sampling Theory: Statistics and Inference
Covers sampling theory, statistics, and inference, focusing on the sampling distribution of statistics.
Gauss-Markov Theorem: Optimal Estimation
Explores the Gauss-Markov Theorem and the optimality of Least Squares Estimators in the Gaussian Linear model.
Statistics & Experimental Design
Explores conditional probability, Framingham studies, effect size, t-test, and sampling error in statistics.
Sampling Theory: Statistics for Mathematicians
Covers the theory of sampling, focusing on statistics for mathematicians.
Distribution Theory of Least Squares
Explores the distribution theory of least squares estimators in a Gaussian linear model, focusing on precision and confidence intervals construction.
Nonuniform Distributions: Generation Methods
Explores methods for generating nonuniform distributions, including the rejection method of von Neumann and the central limit theorem.
Statistical Models and Parameter Estimation
Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.
Markov Chains and Algorithm Applications
Covers the fundamentals of Markov chains and their applications in algorithms, focusing on proper coloring and the Metropolis algorithm.
Sufficient Statistics: Understanding Data Compression
Explores sufficient statistics, data compression, and their role in statistical inference, with examples like Bernoulli Trials and exponential families.
Statistical Analysis: Data Exploration and Inference
Covers statistical analysis, emphasizing data exploration and inference to quantify uncertainty and draw conclusions.
Sampling Distributions: Estimation
Explores sampling distributions, estimation methods, and consistency in parameter estimation.
Sampling Distributions: Theory and Applications
Explores sampling theory, limiting distributions, and estimation for statistical analysis.
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