Bayesian Inference: Estimation & Demystification

Related lectures (30)

Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

Covers inference problems related to the Spin Glass Game and the challenges of making mistakes with preb P.

Explores Bayesian inference for precision in the Gaussian model with known mean, using a Gamma prior and discussing subjective vs objective priors.

Covers the fundamentals of Bayesian estimation, focusing on the application of Bayes' Theorem in scalar estimation.

Explores likelihood in probability, maximum likelihood estimation, and its applications in statistical inference.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Discusses Bayesian inference for the mean of a Gaussian distribution with known variance, covering posterior mean, variance, and MAP estimator.

Explores linear regression analysis of ozone data using statistical models.

Covers maximum likelihood estimation, likelihood function, parameter estimation, and hypothesis testing.

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