Stochastic Simulations: Ergodicity and Estimators

Related lectures (32)

Page 1 of 4

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

Explores the criteria for good estimators, emphasizing consistency and efficiency in estimation.

Covers the concept of geometric ergodicity in the context of convergence diagnostics for Markov chains.

Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.

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

Covers the concept of frequency moments and introduces algorithms for estimating them efficiently.

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

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

Explores point estimation in statistics, discussing bias, variance, mean squared error, and consistency of estimators.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.