Bayesian Inference: Optimal Estimation

Related lectures (31)

Page 3 of 4

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

Covers the estimation of distributions using samples and probability models.

Explores statistical vision problems, evaluating algorithms for hidden signal estimation and the challenges of phase transitions.

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

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

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Covers fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.

Covers conditional probability, illustrating how events influence each other's probabilities and its applications in real-world scenarios.

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.