Lectures related to Correlated and Uncorrelated Sampling

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

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

Explores the joint equidistribution of CM points and their properties in ergodic theory and homogeneous dynamics.

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

Explores correlated sampling, weight functions, Markovian chains, ergodicity, and walkers.

Explores variance, covariance, and practical applications in statistics and probability.

Covers the abstract structure of measure-preserving systems and aims to understand their classification and ergodic decompositions.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Explains errors in correlated and uncorrelated sampling, correlation function, time, and blocking analysis.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

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

Explores sampling theory, limiting distributions, and estimation for statistical analysis.

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Explores memorylessness in distributions, stationary processes, and estimation using MLE.

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Covers the concept of property testing using statistical methods.

Discusses estimation and propagation of uncertainty in random variables and the importance of managing uncertainty in statistical analysis.

Covers probability, statistics, independence, covariance, correlation, and random variables.