Lectures related to Fitting data with one Gauss function

Maximum Likelihood Estimation: Multivariate Statistics

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Estimation Methods in Probability and Statistics

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

Principal Components: Properties & Applications

Explores principal components, covariance, correlation, choice, and applications in data analysis.

Multivariate Statistics: Wishart and Hotelling T²

Explores the Wishart distribution, properties of Wishart matrices, and the Hotelling T² distribution, including the two-sample Hotelling T² statistic.

Maximum Likelihood Estimation

Covers maximum likelihood estimation to estimate parameters by maximizing prediction accuracy, demonstrating through a simple example and discussing validity through hypothesis testing.

Fisher Information, Cramér-Rao Inequality, MLE

Explains Fisher information, Cramér-Rao inequality, and MLE properties, including invariance and asymptotics.

Bias, Variance, Consistency, EMV

Covers bias, variance, mean squared error, consistency, and maximum likelihood estimation in the Poisson model.

Latent Variable Models

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.

PCA: Derivation and Optimization

Covers the derivation of PCA projection, error minimization, and eigenvector optimization.

Maximum Likelihood Estimation MOOC: Introduction to Discrete Choice Models

Covers maximum likelihood estimation, likelihood function, parameter estimation, and hypothesis testing.

Likelihood Ratio Test: Detection & Estimation

Covers the likelihood ratio test for detection and estimation in statistical analysis.

Variance and Covariance: Properties and Examples

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

Long Memory and ARCH: Time Series

Explores long memory in time series and ARCH models for financial volatility.

Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.

Gaussian Mixture Models: Likelihood and Covariance Matrix Log in to Mediaspace to watch this video

Explores statistical independence, Gaussian Mixture Models, and fitting data with Gaussian functions.

Maximum Likelihood Theory & Applications Log in to Mediaspace to watch this video

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Protein Contact Prediction: Pseudolikelihoods and Potts Models Log in to Mediaspace to watch this video

Covers protein contact prediction using Potts models and pseudolikelihood methods, comparing different approaches for contact prediction in proteins.

Maximum Likelihood Estimation: Econometrics Log in to Mediaspace to watch this video

Introduces Maximum Likelihood Estimation in econometrics, covering principles, properties, applications, and specification tests.

Basic Principles of Point Estimation Log in to Mediaspace to watch this video

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

Maximum Likelihood Estimation: Linear Models Log in to Mediaspace to watch this video

Explores maximum likelihood estimation in linear models, covering Gaussian noise, covariance estimation, and support vector machines for classification problems.