Maximum likelihood estimation

Formal sciences
Statistics
Statistical inference
Mathematical statistics

Related lectures (30)

About
Privacy
Disclaimer

Graph Chatbot

Page 2 of 3

The Likelihood of Data under a Model

Explores the likelihood of data under a model and the concept of maximum likelihood.

Optimality in Decision Theory: Unbiased Estimation

Explores optimality in decision theory and unbiased estimation, emphasizing sufficiency, completeness, and lower bounds for risk.

Likelihood of a spike train

Discusses the likelihood of spike trains based on generative models and log-likelihood calculations from observed data.

L-Moment Estimation: Probability-Weighted Moments

Covers L-moment estimation, probability-weighted moments, and maximum likelihood inference basics.

Maximum Likelihood Estimation: Econometrics

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

Bias, Variance, Consistency, EMV

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

Data Classification: Gaussian Mixture Models

Explores Gaussian Mixture Models for data classification, focusing on denoising signals and estimating original data using likelihood and posteriori approaches.

Basic Principles of Point Estimation

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

Fisher Information, Cramér-Rao Inequality, MLE

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

Maximum Likelihood Estimation

Delves into maximum likelihood estimators, their properties, and asymptotic behavior, emphasizing consistency and asymptotic normality.