Multi-arm Bandits

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 3 of 4

Bayesian Inference: Optimal Estimation

Explores optimal Bayesian inference, denoising, scalar estimation, and phase transitions.

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.

Bayes Estimator, Simulated Annealing and EM

Covers Bayes estimator, Simulated Annealing, and EM for parameter estimation.

Parametric Models

Explores statistical estimation, regression models, and model selection in parametric models.

Spiked Matrix Estimation

Covers the AMP algorithm for spiked matrix estimation and its application to low-rank matrix factorization and GLM models.

Maximum Likelihood Estimation

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.

Bayesian Estimation: Unsupervised Learning & MCMC

Explores Bayesian estimation for unsupervised learning and MCMC, using a Spin Glass Card game example.

Probability and Estimation in Statistics

Introduces probability, estimation methods, linear models, testing, and advanced regression techniques.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Estimation Methods in Probability and Statistics

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