Bayesian Inference: Gaussian Variables

Related lectures (30)

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Introduces multivariate statistics, covering normal distribution properties and characteristic functions.

Covers the multivariate normal distribution, properties, and sampling methods.

Covers the transformations of joint continuous densities and their implications on probability distributions.

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

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Covers the multinomial distribution, joint density, marginal distribution, and conditional distribution.

Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.