Lectures related to Error Estimation in LHS

Estimation Methods in Probability and Statistics

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Estimation and Confidence Intervals

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

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.

Statistics: Hypothesis Testing & Confidence Intervals

Covers hypothesis testing, confidence intervals, data distributions, and statistical significance in data analysis.

Understanding Statistics & Experimental Design

Provides an overview of basic probability theory, ANOVA, t-tests, central limit theorem, metrics, confidence intervals, and non-parametric tests.

Confidence Intervals: Gaussian Estimation

Explores confidence intervals, Gaussian estimation, Cramér-Rao inequality, and Maximum Likelihood Estimators.

Sampling a Probability Distribution

Explores sampling a probability distribution and structure functions in statistical analysis.

Understanding Statistics & Experimental Design

Explores t-tests, confidence intervals, ANOVA, and hypothesis testing in statistics, emphasizing the importance of avoiding false discoveries and understanding the logic behind statistical tests.

Hypothesis Testing and Confidence Intervals: An Overview

Covers hypothesis testing, confidence intervals, and their applications in statistics.

Hypothesis Testing & Confidence Intervals

Covers hypothesis testing, power, confidence intervals, and small sample considerations.

Density of States and Bayesian Inference in Computational Mathematics

Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.

Statistical Inference: Approximate Critical Values and Confidence Intervals

Covers the construction of confidence intervals and approximate critical values in statistical inference.

Stratified Sampling: Theory and Applications Log in to Mediaspace to watch this video

Explores stratified sampling, dividing a population into subgroups for more accurate sampling.

Estimators and Confidence Intervals Log in to Mediaspace to watch this video

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

Estimating Relaxation Time: Variance and Chains Log in to Mediaspace to watch this video

Covers the estimation of relaxation time in chains and the importance of sample sizes.

Estimating Parameters: Confidence Intervals Log in to Mediaspace to watch this video

Explores estimating parameters through confidence intervals in linear regression and statistics.

Confidence Intervals and MLE Limit Theorems Log in to Mediaspace to watch this video

Explores constructing confidence intervals and MLE limit theorems for large samples.

Modeling Elasticity and Coefficients Log in to Mediaspace to watch this video

Explores correlation matrices, regression, variance, confidence intervals, and standardized systems in statistical modeling.

Estimation Criteria Log in to Mediaspace to watch this video

Covers criteria for estimating parameters, emphasizing the importance of consistency, bias, variance, and efficiency of estimators.

Inference and Mixed Models Log in to Mediaspace to watch this video

Covers point estimation, confidence intervals, and hypothesis testing for smooth functions using mixed models and spline smoothing.