Bayesian Inference: Estimation & Demystification

Related lectures (30)

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

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

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

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

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

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Covers the basics of probability theory, random variables, joint probability, and inference.