Concept
Method of moments (statistics)
Related lectures (51)
Convergence in Distribution
Covers the concept of convergence in distribution and its characterization using moments.
Gaussian Process Regression: Probabilistic Linear Regression
Explores Probabilistic Linear Regression and Gaussian Process Regression, emphasizing kernel selection and hyperparameter tuning for accurate predictions.
Estimation Methods: Point Estimate and Maximum Likelihood
Discusses different estimators and how to choose between them, emphasizing calculation and properties.
Sampling Theory: Statistics for Mathematicians
Covers the theory of sampling, focusing on statistics for mathematicians.
Statistical Estimators
Explains statistical estimators for random variables and Gaussian distributions, focusing on error functions for integration.
Moments and Carleman's Theorem
Explores moments in random variables and Carleman's theorem for uniquely determining a random variable by its moments.
Probabilistic Models for Linear Regression
Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.
Exponential Families Sampling
Covers sampling from exponential families, joint distribution, moments, and convergence theorems.
Basics of Risk Analysis and Management
Introduces the basics of risk analysis and management in civil engineering, covering distributions, statistical reminders, and mathematical interpretation techniques.
Estimators and Bias
Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.