Bayesian Statistics: Hypothesis Testing and Estimation

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Related lectures (32)

Statistical Tests for Exponential Families

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

Statistical Inference

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

Maximum Likelihood: Inference and Model Comparison

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

Likelihood Ratio Test: Detection & Estimation

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

Confidence Intervals and Hypothesis Testing

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

Likelihood Ratio Test: Neyman-Pearson Lemma

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

Maximum Likelihood Inference

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

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Relevance Vector Machine: Addressing SVM Shortcomings

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

Estimation & Bayesian Inference

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

Statistical Hypothesis Testing: Unilateral and Bilateral Pairs

Explores unilateral and bilateral pairs in statistical hypothesis testing, covering critical values, test statistics, and p-values.

Dirichlet-Multinomial Model

Discusses the Dirichlet distribution, Bayesian inference, posterior mean and variance, conjugate priors, and predictive distribution in the Dirichlet-Multinomial model.

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