Bayesian Statistics: Hypothesis Testing and Estimation

Related lectures (32)

Page 3 of 4

Covers the optimal statistical tests for exponential families and the use of approximations in hypothesis testing.

Covers likelihood ratio statistic, confidence intervals, and hypothesis testing concepts.

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

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

Explores confidence intervals, hypothesis testing, and ROC curves in statistical analysis.

Explores likelihood ratio tests and the Neyman-Pearson Lemma for statistical hypothesis testing.

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

Covers variational inference and neural networks for classification tasks.

Introduces the Relevance Vector Machine as a sparse solution to SVM shortcomings.

Covers demousing, estimation, Bayesian inference, likelihood, AWGN, and more.